from google.colab import drive
drive.mount("/content/MyDrive")
Mounted at /content/MyDrive
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.image import imread
from sklearn.model_selection import train_test_split
from scipy.stats import zscore
import tensorflow as tf
import keras
tf.__version__
from tensorflow.keras.utils import to_categorical
import cv2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
df1=pd.read_csv('/content/MyDrive/MyDrive/Dataset/NN Project Data - Signal.csv') #Reading the csv file
df1.head() #Showing the first 5 datapoints
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
df1.shape #checking the no of rows and columns in dataset
(1599, 12)
There are 1599 rows and 12 columns
df1.isnull().sum() #check for null values
Parameter 1 0 Parameter 2 0 Parameter 3 0 Parameter 4 0 Parameter 5 0 Parameter 6 0 Parameter 7 0 Parameter 8 0 Parameter 9 0 Parameter 10 0 Parameter 11 0 Signal_Strength 0 dtype: int64
percent_missing = df1.isnull().sum() * 100 / len(df1) #missing value percentage
missing_value_df = pd.DataFrame({'column_name': df1.columns,
'percent_missing': percent_missing})
missing_value_df
| column_name | percent_missing | |
|---|---|---|
| Parameter 1 | Parameter 1 | 0.0 |
| Parameter 2 | Parameter 2 | 0.0 |
| Parameter 3 | Parameter 3 | 0.0 |
| Parameter 4 | Parameter 4 | 0.0 |
| Parameter 5 | Parameter 5 | 0.0 |
| Parameter 6 | Parameter 6 | 0.0 |
| Parameter 7 | Parameter 7 | 0.0 |
| Parameter 8 | Parameter 8 | 0.0 |
| Parameter 9 | Parameter 9 | 0.0 |
| Parameter 10 | Parameter 10 | 0.0 |
| Parameter 11 | Parameter 11 | 0.0 |
| Signal_Strength | Signal_Strength | 0.0 |
There are no null values in the dataset
df2=df1.copy(deep=True)
df2.info() #info of the complete dataset
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Parameter 1 1599 non-null float64 1 Parameter 2 1599 non-null float64 2 Parameter 3 1599 non-null float64 3 Parameter 4 1599 non-null float64 4 Parameter 5 1599 non-null float64 5 Parameter 6 1599 non-null float64 6 Parameter 7 1599 non-null float64 7 Parameter 8 1599 non-null float64 8 Parameter 9 1599 non-null float64 9 Parameter 10 1599 non-null float64 10 Parameter 11 1599 non-null float64 11 Signal_Strength 1599 non-null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB
df2.duplicated().sum()
240
As per the given dataset it is possible to contain duplicate values. Hence further imputations are not performed.
df2.describe().T #Statistical summary
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Parameter 1 | 1599.0 | 8.319637 | 1.741096 | 4.60000 | 7.1000 | 7.90000 | 9.200000 | 15.90000 |
| Parameter 2 | 1599.0 | 0.527821 | 0.179060 | 0.12000 | 0.3900 | 0.52000 | 0.640000 | 1.58000 |
| Parameter 3 | 1599.0 | 0.270976 | 0.194801 | 0.00000 | 0.0900 | 0.26000 | 0.420000 | 1.00000 |
| Parameter 4 | 1599.0 | 2.538806 | 1.409928 | 0.90000 | 1.9000 | 2.20000 | 2.600000 | 15.50000 |
| Parameter 5 | 1599.0 | 0.087467 | 0.047065 | 0.01200 | 0.0700 | 0.07900 | 0.090000 | 0.61100 |
| Parameter 6 | 1599.0 | 15.874922 | 10.460157 | 1.00000 | 7.0000 | 14.00000 | 21.000000 | 72.00000 |
| Parameter 7 | 1599.0 | 46.467792 | 32.895324 | 6.00000 | 22.0000 | 38.00000 | 62.000000 | 289.00000 |
| Parameter 8 | 1599.0 | 0.996747 | 0.001887 | 0.99007 | 0.9956 | 0.99675 | 0.997835 | 1.00369 |
| Parameter 9 | 1599.0 | 3.311113 | 0.154386 | 2.74000 | 3.2100 | 3.31000 | 3.400000 | 4.01000 |
| Parameter 10 | 1599.0 | 0.658149 | 0.169507 | 0.33000 | 0.5500 | 0.62000 | 0.730000 | 2.00000 |
| Parameter 11 | 1599.0 | 10.422983 | 1.065668 | 8.40000 | 9.5000 | 10.20000 | 11.100000 | 14.90000 |
| Signal_Strength | 1599.0 | 5.636023 | 0.807569 | 3.00000 | 5.0000 | 6.00000 | 6.000000 | 8.00000 |
sns.countplot(data=df2,x='Signal_Strength');
Signal 5 and 6 has higher counts
correlation_values=df2.corr()['Signal_Strength'] #Correlation of features with target variable
correlation_values.abs().sort_values(ascending=False)
Signal_Strength 1.000000 Parameter 11 0.476166 Parameter 2 0.390558 Parameter 10 0.251397 Parameter 3 0.226373 Parameter 7 0.185100 Parameter 8 0.174919 Parameter 5 0.128907 Parameter 1 0.124052 Parameter 9 0.057731 Parameter 6 0.050656 Parameter 4 0.013732 Name: Signal_Strength, dtype: float64
plt.figure(figsize = (15,7))
sns.heatmap(df2.corr(), cmap='plasma',annot=True, fmt='.2f');
plt.figure(figsize=(20,6));
plt.subplot(1,3,1);
sns.distplot(df2['Parameter 7'],color='green');
plt.title('Parameter 7')
plt.subplot(1,3,2);
sns.distplot(df2['Parameter 9'],color='blue');
plt.title('Parameter 9')
plt.subplot(1,3,3);
sns.distplot(df2['Parameter 11'],color='red');
plt.title('Parameter 11')
plt.figure(figsize=(20,6));
plt.subplot(1,3,1);
sns.boxplot(y=df2['Parameter 5'],color='yellow');
plt.title('Parameter 5')
plt.subplot(1,3,2);
sns.boxplot(y=df2['Parameter 6'],color='orange');
plt.title('Parameter 6')
plt.subplot(1,3,3);
sns.boxplot(y=df2['Signal_Strength'],color='red');
plt.title('Signal_Strength')
Text(0.5, 1.0, 'Signal_Strength')
plt.figure(figsize=(20,6));
plt.subplot(1,3,1);
sns.distplot(df2['Parameter 5'],color='green');
plt.title('Parameter 5')
plt.subplot(1,3,2);
sns.distplot(df2['Parameter 6'],color='blue');
plt.title('Parameter 6')
plt.subplot(1,3,3);
sns.distplot(df2['Signal_Strength'],color='red');
plt.title('Signal_Strength')
plt.figure(figsize=(20,6));
plt.subplot(1,3,1);
sns.boxplot(y=df2['Parameter 7'],color='yellow');
plt.title('Parameter 7')
plt.subplot(1,3,2);
sns.boxplot(y=df2['Parameter 9'],color='orange');
plt.title('Parameter 9')
plt.subplot(1,3,3);
sns.boxplot(y=df2['Parameter 11'],color='red');
plt.title('Parameter 11')
Text(0.5, 1.0, 'Parameter 11')
The above plots presents the distribution and boxplots for few features in the given dataset
sns.scatterplot(x=df2['Parameter 3'],y=df2['Parameter 1'], hue=df2['Signal_Strength']);
sns.scatterplot(x=df2['Parameter 9'],y=df2['Parameter 1'], hue=df2['Signal_Strength']);
sns.scatterplot(x=df2['Parameter 11'],y=df2['Parameter 8'], hue=df2['Signal_Strength']);
sns.scatterplot(x=df2['Parameter 3'],y=df2['Parameter 9'], hue=df2['Signal_Strength']);
The above scatterplots shows relation between the parameters in given dataset
sns.stripplot(data=df2,y='Parameter 5',x='Signal_Strength');
sns.stripplot(data=df2,y='Parameter 6',x='Signal_Strength');
The strip plot shows that different parameters emits varying signals
sns.jointplot(data=df2,x='Parameter 3',y='Parameter 1',kind='hex');
sns.jointplot(data=df2,x='Parameter 6',y='Parameter 7',hue='Signal_Strength');
A few insights from the above plots:
sns.pairplot(data=df2, diag_kind='kde'); #Pair plot
x=df2.drop('Signal_Strength',axis=1)
y=df2['Signal_Strength']
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.30, random_state=1) #Split the data into train and test data of 70:30 ratio
print('X Train set contains {} data'.format(x_train.shape))
print('X Test set contains {} data'.format(x_test.shape))
print('Y Train set contains {} data'.format(y_train.shape))
print('Y Test set contains {} data'.format(y_test.shape))
X Train set contains (1119, 11) data X Test set contains (480, 11) data Y Train set contains (1119,) data Y Test set contains (480,) data
The train and test data numbers are in sync
x_train=x_train.apply(zscore) #Normalize the data
x_test=x_test.apply(zscore)
num_classes = 10 #convert the target variable to one hot vectors
y_train_cat = to_categorical(y_train, num_classes)
y_test_cat=to_categorical(y_test,num_classes)
print("First 5 training lables as one-hot encoded vectors:\n", y_train_cat[:5])
First 5 training lables as one-hot encoded vectors: [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]
from keras import losses
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense,LeakyReLU
# create model
model = Sequential()
model.add(Dense(128, activation='relu',kernel_initializer='normal',input_shape=(11,))) ###Multiple Dense layers with Relu activation
model.add(Dense(64, activation='relu',kernel_initializer='normal'))
model.add(Dense(32, activation='relu',kernel_initializer='normal'))
model.add(LeakyReLU(alpha=0.1))
model.add(Dense(16, activation='relu',kernel_initializer='normal'))
model.add(LeakyReLU(alpha=0.1))
model.add(Dense(num_classes, activation='softmax')) ### For multiclass classification Softmax activation function is used
adam = optimizers.Adam(learning_rate=1e-3)
model.compile(loss='mean_absolute_error', optimizer=adam, metrics=['accuracy']) ### Loss function = MSE
model.summary() #Summary of the neural network model
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 128) 1536
dense_1 (Dense) (None, 64) 8256
dense_2 (Dense) (None, 32) 2080
leaky_re_lu (LeakyReLU) (None, 32) 0
dense_3 (Dense) (None, 16) 528
leaky_re_lu_1 (LeakyReLU) (None, 16) 0
dense_4 (Dense) (None, 10) 170
=================================================================
Total params: 12,570
Trainable params: 12,570
Non-trainable params: 0
_________________________________________________________________
# Fit the model
history=model.fit(x_train, y_train_cat, validation_data=(x_test,y_test_cat), epochs=400, batch_size=200, verbose=2)
Epoch 1/400 6/6 - 4s - loss: 0.1798 - accuracy: 0.3816 - val_loss: 0.1796 - val_accuracy: 0.4042 - 4s/epoch - 623ms/step Epoch 2/400 6/6 - 0s - loss: 0.1794 - accuracy: 0.3959 - val_loss: 0.1789 - val_accuracy: 0.4062 - 50ms/epoch - 8ms/step Epoch 3/400 6/6 - 0s - loss: 0.1785 - accuracy: 0.3959 - val_loss: 0.1777 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 4/400 6/6 - 0s - loss: 0.1770 - accuracy: 0.3959 - val_loss: 0.1752 - val_accuracy: 0.4062 - 49ms/epoch - 8ms/step Epoch 5/400 6/6 - 0s - loss: 0.1734 - accuracy: 0.3959 - val_loss: 0.1693 - val_accuracy: 0.4062 - 60ms/epoch - 10ms/step Epoch 6/400 6/6 - 0s - loss: 0.1652 - accuracy: 0.3959 - val_loss: 0.1560 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 7/400 6/6 - 0s - loss: 0.1489 - accuracy: 0.3959 - val_loss: 0.1353 - val_accuracy: 0.4062 - 64ms/epoch - 11ms/step Epoch 8/400 6/6 - 0s - loss: 0.1298 - accuracy: 0.3959 - val_loss: 0.1217 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 9/400 6/6 - 0s - loss: 0.1219 - accuracy: 0.3959 - val_loss: 0.1185 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 10/400 6/6 - 0s - loss: 0.1200 - accuracy: 0.3959 - val_loss: 0.1176 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 11/400 6/6 - 0s - loss: 0.1192 - accuracy: 0.3959 - val_loss: 0.1163 - val_accuracy: 0.4062 - 57ms/epoch - 10ms/step Epoch 12/400 6/6 - 0s - loss: 0.1176 - accuracy: 0.3959 - val_loss: 0.1136 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 13/400 6/6 - 0s - loss: 0.1146 - accuracy: 0.3959 - val_loss: 0.1113 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 14/400 6/6 - 0s - loss: 0.1136 - accuracy: 0.4307 - val_loss: 0.1098 - val_accuracy: 0.4854 - 52ms/epoch - 9ms/step Epoch 15/400 6/6 - 0s - loss: 0.1115 - accuracy: 0.4227 - val_loss: 0.1075 - val_accuracy: 0.4062 - 55ms/epoch - 9ms/step Epoch 16/400 6/6 - 0s - loss: 0.1094 - accuracy: 0.4173 - val_loss: 0.1039 - val_accuracy: 0.5021 - 45ms/epoch - 7ms/step Epoch 17/400 6/6 - 0s - loss: 0.1058 - accuracy: 0.5362 - val_loss: 0.0987 - val_accuracy: 0.6104 - 44ms/epoch - 7ms/step Epoch 18/400 6/6 - 0s - loss: 0.1008 - accuracy: 0.5702 - val_loss: 0.0932 - val_accuracy: 0.6146 - 44ms/epoch - 7ms/step Epoch 19/400 6/6 - 0s - loss: 0.0956 - accuracy: 0.5719 - val_loss: 0.0877 - val_accuracy: 0.5917 - 43ms/epoch - 7ms/step Epoch 20/400 6/6 - 0s - loss: 0.0912 - accuracy: 0.5710 - val_loss: 0.0845 - val_accuracy: 0.6000 - 70ms/epoch - 12ms/step Epoch 21/400 6/6 - 0s - loss: 0.0883 - accuracy: 0.5827 - val_loss: 0.0828 - val_accuracy: 0.6062 - 59ms/epoch - 10ms/step Epoch 22/400 6/6 - 0s - loss: 0.0865 - accuracy: 0.5836 - val_loss: 0.0821 - val_accuracy: 0.6042 - 56ms/epoch - 9ms/step Epoch 23/400 6/6 - 0s - loss: 0.0854 - accuracy: 0.5889 - val_loss: 0.0818 - val_accuracy: 0.5979 - 44ms/epoch - 7ms/step Epoch 24/400 6/6 - 0s - loss: 0.0844 - accuracy: 0.5880 - val_loss: 0.0815 - val_accuracy: 0.5958 - 46ms/epoch - 8ms/step Epoch 25/400 6/6 - 0s - loss: 0.0838 - accuracy: 0.5871 - val_loss: 0.0816 - val_accuracy: 0.5958 - 46ms/epoch - 8ms/step Epoch 26/400 6/6 - 0s - loss: 0.0832 - accuracy: 0.5925 - val_loss: 0.0818 - val_accuracy: 0.5875 - 61ms/epoch - 10ms/step Epoch 27/400 6/6 - 0s - loss: 0.0829 - accuracy: 0.5898 - val_loss: 0.0814 - val_accuracy: 0.5917 - 62ms/epoch - 10ms/step Epoch 28/400 6/6 - 0s - loss: 0.0823 - accuracy: 0.5943 - val_loss: 0.0822 - val_accuracy: 0.5771 - 63ms/epoch - 10ms/step Epoch 29/400 6/6 - 0s - loss: 0.0820 - accuracy: 0.5952 - val_loss: 0.0819 - val_accuracy: 0.5854 - 60ms/epoch - 10ms/step Epoch 30/400 6/6 - 0s - loss: 0.0818 - accuracy: 0.6014 - val_loss: 0.0801 - val_accuracy: 0.6083 - 56ms/epoch - 9ms/step Epoch 31/400 6/6 - 0s - loss: 0.0814 - accuracy: 0.5996 - val_loss: 0.0802 - val_accuracy: 0.5958 - 45ms/epoch - 8ms/step Epoch 32/400 6/6 - 0s - loss: 0.0815 - accuracy: 0.5979 - val_loss: 0.0812 - val_accuracy: 0.5896 - 63ms/epoch - 10ms/step Epoch 33/400 6/6 - 0s - loss: 0.0811 - accuracy: 0.5996 - val_loss: 0.0803 - val_accuracy: 0.6021 - 49ms/epoch - 8ms/step Epoch 34/400 6/6 - 0s - loss: 0.0804 - accuracy: 0.6077 - val_loss: 0.0805 - val_accuracy: 0.5958 - 45ms/epoch - 8ms/step Epoch 35/400 6/6 - 0s - loss: 0.0805 - accuracy: 0.6041 - val_loss: 0.0805 - val_accuracy: 0.5958 - 61ms/epoch - 10ms/step Epoch 36/400 6/6 - 0s - loss: 0.0798 - accuracy: 0.6148 - val_loss: 0.0795 - val_accuracy: 0.6042 - 49ms/epoch - 8ms/step Epoch 37/400 6/6 - 0s - loss: 0.0799 - accuracy: 0.6095 - val_loss: 0.0791 - val_accuracy: 0.6104 - 59ms/epoch - 10ms/step Epoch 38/400 6/6 - 0s - loss: 0.0795 - accuracy: 0.6095 - val_loss: 0.0794 - val_accuracy: 0.6062 - 58ms/epoch - 10ms/step Epoch 39/400 6/6 - 0s - loss: 0.0788 - accuracy: 0.6166 - val_loss: 0.0795 - val_accuracy: 0.6083 - 53ms/epoch - 9ms/step Epoch 40/400 6/6 - 0s - loss: 0.0786 - accuracy: 0.6193 - val_loss: 0.0798 - val_accuracy: 0.6062 - 59ms/epoch - 10ms/step Epoch 41/400 6/6 - 0s - loss: 0.0782 - accuracy: 0.6175 - val_loss: 0.0798 - val_accuracy: 0.6000 - 60ms/epoch - 10ms/step Epoch 42/400 6/6 - 0s - loss: 0.0784 - accuracy: 0.6139 - val_loss: 0.0795 - val_accuracy: 0.6000 - 45ms/epoch - 8ms/step Epoch 43/400 6/6 - 0s - loss: 0.0777 - accuracy: 0.6202 - val_loss: 0.0788 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 44/400 6/6 - 0s - loss: 0.0775 - accuracy: 0.6220 - val_loss: 0.0792 - val_accuracy: 0.6042 - 43ms/epoch - 7ms/step Epoch 45/400 6/6 - 0s - loss: 0.0773 - accuracy: 0.6202 - val_loss: 0.0790 - val_accuracy: 0.6083 - 44ms/epoch - 7ms/step Epoch 46/400 6/6 - 0s - loss: 0.0770 - accuracy: 0.6229 - val_loss: 0.0784 - val_accuracy: 0.6146 - 60ms/epoch - 10ms/step Epoch 47/400 6/6 - 0s - loss: 0.0769 - accuracy: 0.6220 - val_loss: 0.0787 - val_accuracy: 0.6083 - 43ms/epoch - 7ms/step Epoch 48/400 6/6 - 0s - loss: 0.0766 - accuracy: 0.6229 - val_loss: 0.0782 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 49/400 6/6 - 0s - loss: 0.0763 - accuracy: 0.6273 - val_loss: 0.0783 - val_accuracy: 0.6125 - 56ms/epoch - 9ms/step Epoch 50/400 6/6 - 0s - loss: 0.0760 - accuracy: 0.6256 - val_loss: 0.0788 - val_accuracy: 0.6062 - 45ms/epoch - 7ms/step Epoch 51/400 6/6 - 0s - loss: 0.0756 - accuracy: 0.6282 - val_loss: 0.0786 - val_accuracy: 0.6104 - 64ms/epoch - 11ms/step Epoch 52/400 6/6 - 0s - loss: 0.0753 - accuracy: 0.6309 - val_loss: 0.0786 - val_accuracy: 0.6104 - 44ms/epoch - 7ms/step Epoch 53/400 6/6 - 0s - loss: 0.0752 - accuracy: 0.6291 - val_loss: 0.0788 - val_accuracy: 0.6083 - 44ms/epoch - 7ms/step Epoch 54/400 6/6 - 0s - loss: 0.0749 - accuracy: 0.6327 - val_loss: 0.0783 - val_accuracy: 0.6083 - 65ms/epoch - 11ms/step Epoch 55/400 6/6 - 0s - loss: 0.0748 - accuracy: 0.6345 - val_loss: 0.0784 - val_accuracy: 0.6083 - 42ms/epoch - 7ms/step Epoch 56/400 6/6 - 0s - loss: 0.0745 - accuracy: 0.6345 - val_loss: 0.0783 - val_accuracy: 0.6042 - 43ms/epoch - 7ms/step Epoch 57/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6354 - val_loss: 0.0784 - val_accuracy: 0.6104 - 44ms/epoch - 7ms/step Epoch 58/400 6/6 - 0s - loss: 0.0745 - accuracy: 0.6336 - val_loss: 0.0780 - val_accuracy: 0.6104 - 58ms/epoch - 10ms/step Epoch 59/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6354 - val_loss: 0.0780 - val_accuracy: 0.6125 - 43ms/epoch - 7ms/step Epoch 60/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6354 - val_loss: 0.0781 - val_accuracy: 0.6104 - 58ms/epoch - 10ms/step Epoch 61/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6354 - val_loss: 0.0778 - val_accuracy: 0.6146 - 74ms/epoch - 12ms/step Epoch 62/400 6/6 - 0s - loss: 0.0740 - accuracy: 0.6363 - val_loss: 0.0779 - val_accuracy: 0.6125 - 63ms/epoch - 11ms/step Epoch 63/400 6/6 - 0s - loss: 0.0739 - accuracy: 0.6354 - val_loss: 0.0780 - val_accuracy: 0.6104 - 59ms/epoch - 10ms/step Epoch 64/400 6/6 - 0s - loss: 0.0736 - accuracy: 0.6354 - val_loss: 0.0778 - val_accuracy: 0.6125 - 61ms/epoch - 10ms/step Epoch 65/400 6/6 - 0s - loss: 0.0736 - accuracy: 0.6363 - val_loss: 0.0776 - val_accuracy: 0.6167 - 72ms/epoch - 12ms/step Epoch 66/400 6/6 - 0s - loss: 0.0734 - accuracy: 0.6372 - val_loss: 0.0776 - val_accuracy: 0.6146 - 84ms/epoch - 14ms/step Epoch 67/400 6/6 - 0s - loss: 0.0736 - accuracy: 0.6345 - val_loss: 0.0771 - val_accuracy: 0.6187 - 71ms/epoch - 12ms/step Epoch 68/400 6/6 - 0s - loss: 0.0734 - accuracy: 0.6372 - val_loss: 0.0777 - val_accuracy: 0.6104 - 69ms/epoch - 11ms/step Epoch 69/400 6/6 - 0s - loss: 0.0734 - accuracy: 0.6354 - val_loss: 0.0776 - val_accuracy: 0.6187 - 69ms/epoch - 12ms/step Epoch 70/400 6/6 - 0s - loss: 0.0733 - accuracy: 0.6372 - val_loss: 0.0773 - val_accuracy: 0.6104 - 57ms/epoch - 10ms/step Epoch 71/400 6/6 - 0s - loss: 0.0733 - accuracy: 0.6363 - val_loss: 0.0772 - val_accuracy: 0.6167 - 70ms/epoch - 12ms/step Epoch 72/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6372 - val_loss: 0.0776 - val_accuracy: 0.6167 - 69ms/epoch - 12ms/step Epoch 73/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6372 - val_loss: 0.0775 - val_accuracy: 0.6104 - 69ms/epoch - 11ms/step Epoch 74/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6372 - val_loss: 0.0771 - val_accuracy: 0.6125 - 73ms/epoch - 12ms/step Epoch 75/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6372 - val_loss: 0.0772 - val_accuracy: 0.6187 - 76ms/epoch - 13ms/step Epoch 76/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6372 - val_loss: 0.0770 - val_accuracy: 0.6146 - 71ms/epoch - 12ms/step Epoch 77/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6381 - val_loss: 0.0771 - val_accuracy: 0.6187 - 72ms/epoch - 12ms/step Epoch 78/400 6/6 - 0s - loss: 0.0729 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6146 - 72ms/epoch - 12ms/step Epoch 79/400 6/6 - 0s - loss: 0.0730 - accuracy: 0.6381 - val_loss: 0.0771 - val_accuracy: 0.6125 - 70ms/epoch - 12ms/step Epoch 80/400 6/6 - 0s - loss: 0.0729 - accuracy: 0.6381 - val_loss: 0.0769 - val_accuracy: 0.6125 - 72ms/epoch - 12ms/step Epoch 81/400 6/6 - 0s - loss: 0.0728 - accuracy: 0.6381 - val_loss: 0.0768 - val_accuracy: 0.6208 - 66ms/epoch - 11ms/step Epoch 82/400 6/6 - 0s - loss: 0.0727 - accuracy: 0.6381 - val_loss: 0.0768 - val_accuracy: 0.6146 - 73ms/epoch - 12ms/step Epoch 83/400 6/6 - 0s - loss: 0.0727 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6167 - 82ms/epoch - 14ms/step Epoch 84/400 6/6 - 0s - loss: 0.0727 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6125 - 70ms/epoch - 12ms/step Epoch 85/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6167 - 58ms/epoch - 10ms/step Epoch 86/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6187 - 69ms/epoch - 12ms/step Epoch 87/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6167 - 60ms/epoch - 10ms/step Epoch 88/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6381 - val_loss: 0.0771 - val_accuracy: 0.6167 - 71ms/epoch - 12ms/step Epoch 89/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6381 - val_loss: 0.0770 - val_accuracy: 0.6187 - 69ms/epoch - 11ms/step Epoch 90/400 6/6 - 0s - loss: 0.0725 - accuracy: 0.6381 - val_loss: 0.0769 - val_accuracy: 0.6167 - 63ms/epoch - 10ms/step Epoch 91/400 6/6 - 0s - loss: 0.0725 - accuracy: 0.6390 - val_loss: 0.0768 - 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0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 117/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 49ms/epoch - 8ms/step Epoch 118/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 59ms/epoch - 10ms/step Epoch 119/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 120/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 121/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 122/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 123/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 57ms/epoch - 9ms/step Epoch 124/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 56ms/epoch - 9ms/step Epoch 125/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0771 - val_accuracy: 0.6146 - 47ms/epoch - 8ms/step Epoch 126/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 127/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0767 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 128/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 129/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 51ms/epoch - 8ms/step Epoch 130/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0767 - val_accuracy: 0.6187 - 59ms/epoch - 10ms/step Epoch 131/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 48ms/epoch - 8ms/step Epoch 132/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 60ms/epoch - 10ms/step Epoch 133/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6167 - 57ms/epoch - 9ms/step Epoch 134/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 62ms/epoch - 10ms/step Epoch 135/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 52ms/epoch - 9ms/step Epoch 136/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 46ms/epoch - 8ms/step Epoch 137/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6167 - 46ms/epoch - 8ms/step Epoch 138/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 47ms/epoch - 8ms/step Epoch 139/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6167 - 59ms/epoch - 10ms/step Epoch 140/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 47ms/epoch - 8ms/step Epoch 141/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 142/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 47ms/epoch - 8ms/step Epoch 143/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6187 - 58ms/epoch - 10ms/step Epoch 144/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 145/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 146/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 57ms/epoch - 9ms/step Epoch 147/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0767 - val_accuracy: 0.6187 - 57ms/epoch - 9ms/step Epoch 148/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 58ms/epoch - 10ms/step Epoch 149/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 150/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 151/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 43ms/epoch - 7ms/step Epoch 152/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 153/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 57ms/epoch - 9ms/step Epoch 154/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 47ms/epoch - 8ms/step Epoch 155/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 156/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 59ms/epoch - 10ms/step Epoch 157/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 158/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 159/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 160/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 161/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 47ms/epoch - 8ms/step Epoch 162/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 59ms/epoch - 10ms/step Epoch 163/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 57ms/epoch - 9ms/step Epoch 164/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6167 - 60ms/epoch - 10ms/step Epoch 165/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6167 - 60ms/epoch - 10ms/step Epoch 166/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 67ms/epoch - 11ms/step Epoch 167/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 168/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 169/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 63ms/epoch - 11ms/step Epoch 170/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 171/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 56ms/epoch - 9ms/step Epoch 172/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 57ms/epoch - 10ms/step Epoch 173/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 46ms/epoch - 8ms/step Epoch 174/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6167 - 45ms/epoch - 7ms/step Epoch 175/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 46ms/epoch - 8ms/step Epoch 176/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 45ms/epoch - 7ms/step Epoch 177/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 44ms/epoch - 7ms/step Epoch 178/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 43ms/epoch - 7ms/step Epoch 179/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 180/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 181/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 42ms/epoch - 7ms/step Epoch 182/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 58ms/epoch - 10ms/step Epoch 183/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6187 - 64ms/epoch - 11ms/step Epoch 184/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6187 - 59ms/epoch - 10ms/step Epoch 185/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6187 - 57ms/epoch - 9ms/step Epoch 186/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6187 - 43ms/epoch - 7ms/step Epoch 187/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6187 - 47ms/epoch - 8ms/step Epoch 188/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 57ms/epoch - 10ms/step Epoch 189/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6187 - 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10ms/step Epoch 321/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 70ms/epoch - 12ms/step Epoch 322/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6167 - 72ms/epoch - 12ms/step Epoch 323/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 75ms/epoch - 12ms/step Epoch 324/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 72ms/epoch - 12ms/step Epoch 325/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0770 - val_accuracy: 0.6146 - 71ms/epoch - 12ms/step Epoch 326/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 64ms/epoch - 11ms/step Epoch 327/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 72ms/epoch - 12ms/step Epoch 328/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 71ms/epoch - 12ms/step Epoch 329/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0770 - val_accuracy: 0.6146 - 72ms/epoch - 12ms/step Epoch 330/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0770 - val_accuracy: 0.6146 - 68ms/epoch - 11ms/step Epoch 331/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 80ms/epoch - 13ms/step Epoch 332/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 75ms/epoch - 13ms/step Epoch 333/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0769 - val_accuracy: 0.6146 - 73ms/epoch - 12ms/step Epoch 334/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0770 - val_accuracy: 0.6125 - 76ms/epoch - 13ms/step Epoch 335/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0770 - val_accuracy: 0.6125 - 75ms/epoch - 13ms/step Epoch 336/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6399 - val_loss: 0.0768 - val_accuracy: 0.6146 - 48ms/epoch - 8ms/step Epoch 337/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0784 - val_accuracy: 0.6104 - 51ms/epoch - 9ms/step Epoch 338/400 6/6 - 0s - loss: 0.0723 - accuracy: 0.6381 - val_loss: 0.0769 - val_accuracy: 0.6167 - 45ms/epoch - 7ms/step Epoch 339/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6336 - val_loss: 0.0774 - val_accuracy: 0.6146 - 44ms/epoch - 7ms/step Epoch 340/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6291 - val_loss: 0.0764 - val_accuracy: 0.6187 - 43ms/epoch - 7ms/step Epoch 341/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6345 - val_loss: 0.0775 - val_accuracy: 0.6125 - 43ms/epoch - 7ms/step Epoch 342/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6291 - val_loss: 0.0773 - val_accuracy: 0.6125 - 57ms/epoch - 9ms/step Epoch 343/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6291 - val_loss: 0.0779 - val_accuracy: 0.6083 - 47ms/epoch - 8ms/step Epoch 344/400 6/6 - 0s - loss: 0.0728 - accuracy: 0.6372 - val_loss: 0.0793 - val_accuracy: 0.6042 - 46ms/epoch - 8ms/step Epoch 345/400 6/6 - 0s - loss: 0.0752 - accuracy: 0.6256 - val_loss: 0.0790 - val_accuracy: 0.6062 - 61ms/epoch - 10ms/step Epoch 346/400 6/6 - 0s - loss: 0.0757 - accuracy: 0.6220 - val_loss: 0.0772 - val_accuracy: 0.6146 - 64ms/epoch - 11ms/step Epoch 347/400 6/6 - 0s - loss: 0.0776 - accuracy: 0.6122 - val_loss: 0.0775 - val_accuracy: 0.6146 - 56ms/epoch - 9ms/step Epoch 348/400 6/6 - 0s - loss: 0.0779 - accuracy: 0.6113 - val_loss: 0.0789 - val_accuracy: 0.6021 - 44ms/epoch - 7ms/step Epoch 349/400 6/6 - 0s - loss: 0.0783 - accuracy: 0.6095 - val_loss: 0.0800 - val_accuracy: 0.5979 - 57ms/epoch - 10ms/step Epoch 350/400 6/6 - 0s - loss: 0.0779 - accuracy: 0.6113 - val_loss: 0.0787 - val_accuracy: 0.6083 - 41ms/epoch - 7ms/step Epoch 351/400 6/6 - 0s - loss: 0.0772 - accuracy: 0.6130 - val_loss: 0.0789 - val_accuracy: 0.6042 - 60ms/epoch - 10ms/step Epoch 352/400 6/6 - 0s - loss: 0.0771 - accuracy: 0.6157 - val_loss: 0.0791 - val_accuracy: 0.6042 - 43ms/epoch - 7ms/step Epoch 353/400 6/6 - 0s - loss: 0.0763 - accuracy: 0.6193 - val_loss: 0.0801 - val_accuracy: 0.5979 - 54ms/epoch - 9ms/step Epoch 354/400 6/6 - 0s - loss: 0.0761 - accuracy: 0.6193 - val_loss: 0.0776 - val_accuracy: 0.6104 - 42ms/epoch - 7ms/step Epoch 355/400 6/6 - 0s - loss: 0.0759 - accuracy: 0.6211 - val_loss: 0.0771 - val_accuracy: 0.6146 - 49ms/epoch - 8ms/step Epoch 356/400 6/6 - 0s - loss: 0.0760 - accuracy: 0.6202 - val_loss: 0.0789 - val_accuracy: 0.6042 - 42ms/epoch - 7ms/step Epoch 357/400 6/6 - 0s - loss: 0.0750 - accuracy: 0.6256 - val_loss: 0.0770 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 358/400 6/6 - 0s - loss: 0.0746 - accuracy: 0.6282 - val_loss: 0.0757 - val_accuracy: 0.6208 - 45ms/epoch - 7ms/step Epoch 359/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6300 - val_loss: 0.0759 - val_accuracy: 0.6229 - 44ms/epoch - 7ms/step Epoch 360/400 6/6 - 0s - loss: 0.0737 - accuracy: 0.6336 - val_loss: 0.0774 - val_accuracy: 0.6146 - 43ms/epoch - 7ms/step Epoch 361/400 6/6 - 0s - loss: 0.0730 - accuracy: 0.6354 - val_loss: 0.0769 - val_accuracy: 0.6187 - 57ms/epoch - 10ms/step Epoch 362/400 6/6 - 0s - loss: 0.0728 - accuracy: 0.6381 - val_loss: 0.0772 - val_accuracy: 0.6146 - 46ms/epoch - 8ms/step Epoch 363/400 6/6 - 0s - loss: 0.0730 - accuracy: 0.6345 - val_loss: 0.0775 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 364/400 6/6 - 0s - loss: 0.0735 - accuracy: 0.6327 - val_loss: 0.0776 - val_accuracy: 0.6125 - 59ms/epoch - 10ms/step Epoch 365/400 6/6 - 0s - loss: 0.0722 - accuracy: 0.6399 - val_loss: 0.0772 - val_accuracy: 0.6146 - 59ms/epoch - 10ms/step Epoch 366/400 6/6 - 0s - loss: 0.0714 - accuracy: 0.6434 - val_loss: 0.0790 - val_accuracy: 0.6042 - 59ms/epoch - 10ms/step Epoch 367/400 6/6 - 0s - loss: 0.0712 - accuracy: 0.6452 - val_loss: 0.0777 - val_accuracy: 0.6125 - 66ms/epoch - 11ms/step Epoch 368/400 6/6 - 0s - loss: 0.0710 - accuracy: 0.6452 - val_loss: 0.0771 - val_accuracy: 0.6146 - 45ms/epoch - 7ms/step Epoch 369/400 6/6 - 0s - loss: 0.0709 - accuracy: 0.6461 - val_loss: 0.0775 - val_accuracy: 0.6146 - 59ms/epoch - 10ms/step Epoch 370/400 6/6 - 0s - loss: 0.0707 - accuracy: 0.6470 - val_loss: 0.0782 - val_accuracy: 0.6104 - 45ms/epoch - 8ms/step Epoch 371/400 6/6 - 0s - loss: 0.0707 - accuracy: 0.6470 - val_loss: 0.0774 - val_accuracy: 0.6125 - 55ms/epoch - 9ms/step Epoch 372/400 6/6 - 0s - loss: 0.0709 - accuracy: 0.6461 - val_loss: 0.0774 - val_accuracy: 0.6146 - 57ms/epoch - 9ms/step Epoch 373/400 6/6 - 0s - loss: 0.0708 - accuracy: 0.6461 - val_loss: 0.0772 - val_accuracy: 0.6125 - 60ms/epoch - 10ms/step Epoch 374/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6470 - val_loss: 0.0776 - val_accuracy: 0.6125 - 59ms/epoch - 10ms/step Epoch 375/400 6/6 - 0s - loss: 0.0707 - accuracy: 0.6470 - val_loss: 0.0777 - val_accuracy: 0.6125 - 45ms/epoch - 8ms/step Epoch 376/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6470 - val_loss: 0.0778 - val_accuracy: 0.6104 - 61ms/epoch - 10ms/step Epoch 377/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6470 - val_loss: 0.0778 - val_accuracy: 0.6083 - 44ms/epoch - 7ms/step Epoch 378/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6470 - val_loss: 0.0780 - val_accuracy: 0.6083 - 51ms/epoch - 8ms/step Epoch 379/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6470 - val_loss: 0.0779 - val_accuracy: 0.6083 - 46ms/epoch - 8ms/step Epoch 380/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6470 - val_loss: 0.0778 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 381/400 6/6 - 0s - loss: 0.0705 - accuracy: 0.6479 - val_loss: 0.0777 - val_accuracy: 0.6125 - 46ms/epoch - 8ms/step Epoch 382/400 6/6 - 0s - loss: 0.0705 - accuracy: 0.6479 - val_loss: 0.0777 - val_accuracy: 0.6083 - 59ms/epoch - 10ms/step Epoch 383/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0776 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 384/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0775 - val_accuracy: 0.6125 - 44ms/epoch - 7ms/step Epoch 385/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0776 - val_accuracy: 0.6125 - 43ms/epoch - 7ms/step Epoch 386/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0776 - val_accuracy: 0.6125 - 49ms/epoch - 8ms/step Epoch 387/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0777 - val_accuracy: 0.6104 - 56ms/epoch - 9ms/step Epoch 388/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0777 - val_accuracy: 0.6083 - 43ms/epoch - 7ms/step Epoch 389/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0777 - val_accuracy: 0.6083 - 56ms/epoch - 9ms/step Epoch 390/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0777 - val_accuracy: 0.6083 - 51ms/epoch - 8ms/step Epoch 391/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 42ms/epoch - 7ms/step Epoch 392/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6104 - 56ms/epoch - 9ms/step Epoch 393/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6104 - 45ms/epoch - 8ms/step Epoch 394/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 42ms/epoch - 7ms/step Epoch 395/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 59ms/epoch - 10ms/step Epoch 396/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 43ms/epoch - 7ms/step Epoch 397/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 46ms/epoch - 8ms/step Epoch 398/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 58ms/epoch - 10ms/step Epoch 399/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0778 - val_accuracy: 0.6083 - 43ms/epoch - 7ms/step Epoch 400/400 6/6 - 0s - loss: 0.0704 - accuracy: 0.6479 - val_loss: 0.0779 - val_accuracy: 0.6083 - 44ms/epoch - 7ms/step
# predicting the model on test data
y_pred=model.predict(x_test)
15/15 [==============================] - 0s 2ms/step
y_pred[0]
array([7.5059706e-20, 4.5178044e-15, 1.4451022e-21, 1.1218585e-22,
1.0324295e-13, 2.0835235e-11, 1.0000000e+00, 7.0103475e-16,
8.8083591e-15, 2.2171640e-18], dtype=float32)
# Since the outputs are in probabilities we try to get the label
y_pred_final=[]
for i in y_pred:
y_pred_final.append(np.argmax(i))
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred_final))
precision recall f1-score support
3 0.00 0.00 0.00 2
4 0.00 0.00 0.00 21
5 0.68 0.73 0.71 207
6 0.54 0.72 0.62 195
7 0.00 0.00 0.00 52
8 0.00 0.00 0.00 3
accuracy 0.61 480
macro avg 0.20 0.24 0.22 480
weighted avg 0.52 0.61 0.56 480
from sklearn.metrics import confusion_matrix #Confusion matrix
import seaborn as sns
cm=confusion_matrix(y_test,y_pred_final)
plt.figure(figsize=(10,7))
sns.heatmap(cm,annot=True,fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')
plt.show()
import matplotlib.pyplot as plt #plotting the accuracy and losses after each iteration
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string])
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
from keras import losses
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense,LeakyReLU,Dropout,BatchNormalization
# create model
model = Sequential()
model.add(Dense(128, activation='relu',kernel_initializer='normal',input_shape=(11,))) ###Multiple Dense layers with Relu activation
model.add(Dense(64, activation='relu',kernel_initializer='normal'))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu',kernel_initializer='normal'))
model.add(LeakyReLU(alpha=0.1))
model.add(Dropout(0.5))
model.add(Dense(16, activation='relu',kernel_initializer='normal'))
model.add(LeakyReLU(alpha=0.1))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax')) ### For multiclass classification Softmax activation function is used
adam = optimizers.Adam(learning_rate=1e-3)
model.compile(loss='mean_absolute_error', optimizer=adam, metrics=['accuracy']) ### Loss function = MSE
model.summary() #Summary of the neural network model
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_5 (Dense) (None, 128) 1536
dense_6 (Dense) (None, 64) 8256
dropout (Dropout) (None, 64) 0
dense_7 (Dense) (None, 32) 2080
leaky_re_lu_2 (LeakyReLU) (None, 32) 0
dropout_1 (Dropout) (None, 32) 0
dense_8 (Dense) (None, 16) 528
leaky_re_lu_3 (LeakyReLU) (None, 16) 0
dropout_2 (Dropout) (None, 16) 0
dense_9 (Dense) (None, 10) 170
=================================================================
Total params: 12,570
Trainable params: 12,570
Non-trainable params: 0
_________________________________________________________________
# Fit the model
history=model.fit(x_train, y_train_cat, validation_data=(x_test,y_test_cat), epochs=400, batch_size=200, verbose=2)
Epoch 1/400 6/6 - 1s - loss: 0.1800 - accuracy: 0.1984 - val_loss: 0.1798 - val_accuracy: 0.4062 - 754ms/epoch - 126ms/step Epoch 2/400 6/6 - 0s - loss: 0.1797 - accuracy: 0.3503 - val_loss: 0.1796 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 3/400 6/6 - 0s - loss: 0.1795 - accuracy: 0.3244 - val_loss: 0.1793 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 4/400 6/6 - 0s - loss: 0.1792 - accuracy: 0.3467 - val_loss: 0.1790 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 5/400 6/6 - 0s - loss: 0.1788 - accuracy: 0.3315 - val_loss: 0.1784 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 6/400 6/6 - 0s - loss: 0.1781 - accuracy: 0.3441 - val_loss: 0.1774 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 7/400 6/6 - 0s - loss: 0.1766 - accuracy: 0.3378 - val_loss: 0.1750 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 8/400 6/6 - 0s - loss: 0.1737 - accuracy: 0.3146 - val_loss: 0.1695 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 9/400 6/6 - 0s - loss: 0.1663 - accuracy: 0.3485 - val_loss: 0.1574 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 10/400 6/6 - 0s - loss: 0.1571 - accuracy: 0.3199 - val_loss: 0.1385 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 11/400 6/6 - 0s - loss: 0.1462 - accuracy: 0.3458 - val_loss: 0.1249 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 12/400 6/6 - 0s - loss: 0.1390 - accuracy: 0.3441 - val_loss: 0.1204 - val_accuracy: 0.4062 - 55ms/epoch - 9ms/step Epoch 13/400 6/6 - 0s - loss: 0.1339 - accuracy: 0.3476 - val_loss: 0.1192 - val_accuracy: 0.4062 - 63ms/epoch - 10ms/step Epoch 14/400 6/6 - 0s - loss: 0.1289 - accuracy: 0.3709 - val_loss: 0.1189 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 15/400 6/6 - 0s - loss: 0.1257 - accuracy: 0.3825 - val_loss: 0.1188 - val_accuracy: 0.4062 - 62ms/epoch - 10ms/step Epoch 16/400 6/6 - 0s - loss: 0.1237 - accuracy: 0.3896 - val_loss: 0.1188 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 17/400 6/6 - 0s - loss: 0.1243 - accuracy: 0.3843 - val_loss: 0.1188 - val_accuracy: 0.4062 - 46ms/epoch - 8ms/step Epoch 18/400 6/6 - 0s - loss: 0.1240 - accuracy: 0.3807 - val_loss: 0.1188 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 19/400 6/6 - 0s - loss: 0.1227 - accuracy: 0.3896 - val_loss: 0.1188 - val_accuracy: 0.4062 - 45ms/epoch - 7ms/step Epoch 20/400 6/6 - 0s - loss: 0.1219 - accuracy: 0.3896 - val_loss: 0.1188 - val_accuracy: 0.4062 - 53ms/epoch - 9ms/step Epoch 21/400 6/6 - 0s - loss: 0.1221 - accuracy: 0.3950 - val_loss: 0.1188 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 22/400 6/6 - 0s - loss: 0.1230 - accuracy: 0.3878 - val_loss: 0.1188 - val_accuracy: 0.4062 - 45ms/epoch - 7ms/step Epoch 23/400 6/6 - 0s - loss: 0.1228 - accuracy: 0.3896 - val_loss: 0.1188 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 24/400 6/6 - 0s - loss: 0.1219 - accuracy: 0.3914 - val_loss: 0.1188 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 25/400 6/6 - 0s - loss: 0.1235 - accuracy: 0.3843 - val_loss: 0.1188 - val_accuracy: 0.4062 - 47ms/epoch - 8ms/step Epoch 26/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3959 - val_loss: 0.1188 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 27/400 6/6 - 0s - loss: 0.1216 - accuracy: 0.3941 - val_loss: 0.1188 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 28/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3986 - val_loss: 0.1188 - val_accuracy: 0.4062 - 55ms/epoch - 9ms/step Epoch 29/400 6/6 - 0s - loss: 0.1213 - accuracy: 0.3959 - val_loss: 0.1188 - val_accuracy: 0.4062 - 45ms/epoch - 7ms/step Epoch 30/400 6/6 - 0s - loss: 0.1214 - accuracy: 0.3950 - val_loss: 0.1188 - val_accuracy: 0.4062 - 51ms/epoch - 9ms/step Epoch 31/400 6/6 - 0s - loss: 0.1217 - accuracy: 0.3932 - val_loss: 0.1188 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 32/400 6/6 - 0s - loss: 0.1215 - accuracy: 0.3941 - val_loss: 0.1188 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 33/400 6/6 - 0s - loss: 0.1215 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 63ms/epoch - 11ms/step Epoch 34/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3968 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 35/400 6/6 - 0s - loss: 0.1217 - accuracy: 0.3932 - val_loss: 0.1188 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 36/400 6/6 - 0s - loss: 0.1215 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 48ms/epoch - 8ms/step Epoch 37/400 6/6 - 0s - loss: 0.1213 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 38/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 39/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 40/400 6/6 - 0s - loss: 0.1214 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 64ms/epoch - 11ms/step Epoch 41/400 6/6 - 0s - loss: 0.1216 - accuracy: 0.3932 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 42/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 57ms/epoch - 10ms/step Epoch 43/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 56ms/epoch - 9ms/step Epoch 44/400 6/6 - 0s - loss: 0.1212 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 46ms/epoch - 8ms/step Epoch 45/400 6/6 - 0s - loss: 0.1216 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 46ms/epoch - 8ms/step Epoch 46/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 47/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3968 - val_loss: 0.1187 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 48/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 49/400 6/6 - 0s - loss: 0.1214 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 66ms/epoch - 11ms/step Epoch 50/400 6/6 - 0s - loss: 0.1214 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 51/400 6/6 - 0s - loss: 0.1214 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 52ms/epoch - 9ms/step Epoch 52/400 6/6 - 0s - loss: 0.1215 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 53/400 6/6 - 0s - loss: 0.1215 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 54/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 55/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 56/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 57/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 58/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 47ms/epoch - 8ms/step Epoch 59/400 6/6 - 0s - loss: 0.1212 - accuracy: 0.3941 - val_loss: 0.1187 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 60/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 61/400 6/6 - 0s - loss: 0.1213 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 47ms/epoch - 8ms/step Epoch 62/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 63/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3968 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 64/400 6/6 - 0s - loss: 0.1215 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 65/400 6/6 - 0s - loss: 0.1207 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 66/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 56ms/epoch - 9ms/step Epoch 67/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 61ms/epoch - 10ms/step Epoch 68/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 58ms/epoch - 10ms/step Epoch 69/400 6/6 - 0s - loss: 0.1214 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 57ms/epoch - 10ms/step Epoch 70/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 48ms/epoch - 8ms/step Epoch 71/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 72/400 6/6 - 0s - loss: 0.1213 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 47ms/epoch - 8ms/step Epoch 73/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 74/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 75/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 76/400 6/6 - 0s - loss: 0.1213 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 45ms/epoch - 7ms/step Epoch 77/400 6/6 - 0s - loss: 0.1217 - accuracy: 0.3932 - val_loss: 0.1187 - val_accuracy: 0.4062 - 44ms/epoch - 7ms/step Epoch 78/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 46ms/epoch - 8ms/step Epoch 79/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 60ms/epoch - 10ms/step Epoch 80/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 46ms/epoch - 8ms/step Epoch 81/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 43ms/epoch - 7ms/step Epoch 82/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3968 - val_loss: 0.1187 - val_accuracy: 0.4062 - 45ms/epoch - 8ms/step Epoch 83/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3968 - val_loss: 0.1187 - val_accuracy: 0.4062 - 42ms/epoch - 7ms/step Epoch 84/400 6/6 - 0s - loss: 0.1207 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 73ms/epoch - 12ms/step Epoch 85/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 67ms/epoch - 11ms/step Epoch 86/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 66ms/epoch - 11ms/step Epoch 87/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 69ms/epoch - 11ms/step Epoch 88/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 73ms/epoch - 12ms/step Epoch 89/400 6/6 - 0s - loss: 0.1208 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 72ms/epoch - 12ms/step Epoch 90/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 74ms/epoch - 12ms/step Epoch 91/400 6/6 - 0s - loss: 0.1212 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 59ms/epoch - 10ms/step Epoch 92/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 63ms/epoch - 11ms/step Epoch 93/400 6/6 - 0s - loss: 0.1204 - accuracy: 0.3977 - val_loss: 0.1187 - val_accuracy: 0.4062 - 76ms/epoch - 13ms/step Epoch 94/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 74ms/epoch - 12ms/step Epoch 95/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 65ms/epoch - 11ms/step Epoch 96/400 6/6 - 0s - loss: 0.1210 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 73ms/epoch - 12ms/step Epoch 97/400 6/6 - 0s - loss: 0.1207 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 61ms/epoch - 10ms/step Epoch 98/400 6/6 - 0s - loss: 0.1211 - accuracy: 0.3950 - val_loss: 0.1187 - val_accuracy: 0.4062 - 76ms/epoch - 13ms/step Epoch 99/400 6/6 - 0s - loss: 0.1209 - accuracy: 0.3959 - val_loss: 0.1187 - val_accuracy: 0.4062 - 72ms/epoch - 12ms/step Epoch 100/400 6/6 - 0s - loss: 0.1202 - accuracy: 0.3968 - val_loss: 0.1185 - val_accuracy: 0.4062 - 76ms/epoch - 13ms/step Epoch 101/400 6/6 - 0s - loss: 0.1204 - accuracy: 0.3959 - val_loss: 0.1183 - val_accuracy: 0.4062 - 76ms/epoch - 13ms/step Epoch 102/400 6/6 - 0s - loss: 0.1206 - accuracy: 0.3950 - val_loss: 0.1182 - val_accuracy: 0.4062 - 67ms/epoch - 11ms/step Epoch 103/400 6/6 - 0s - loss: 0.1205 - accuracy: 0.3959 - val_loss: 0.1182 - val_accuracy: 0.4062 - 65ms/epoch - 11ms/step Epoch 104/400 6/6 - 0s - loss: 0.1201 - accuracy: 0.3959 - val_loss: 0.1183 - val_accuracy: 0.4062 - 71ms/epoch - 12ms/step Epoch 105/400 6/6 - 0s - loss: 0.1202 - accuracy: 0.3959 - val_loss: 0.1182 - val_accuracy: 0.4062 - 74ms/epoch - 12ms/step Epoch 106/400 6/6 - 0s - loss: 0.1202 - accuracy: 0.3968 - val_loss: 0.1181 - val_accuracy: 0.4062 - 75ms/epoch - 13ms/step Epoch 107/400 6/6 - 0s - loss: 0.1198 - accuracy: 0.3959 - val_loss: 0.1181 - val_accuracy: 0.4062 - 74ms/epoch - 12ms/step Epoch 108/400 6/6 - 0s - loss: 0.1203 - accuracy: 0.3950 - val_loss: 0.1180 - val_accuracy: 0.4062 - 63ms/epoch - 10ms/step Epoch 109/400 6/6 - 0s - loss: 0.1195 - accuracy: 0.3959 - val_loss: 0.1179 - val_accuracy: 0.4062 - 84ms/epoch - 14ms/step Epoch 110/400 6/6 - 0s - loss: 0.1197 - accuracy: 0.3950 - val_loss: 0.1179 - val_accuracy: 0.4062 - 75ms/epoch - 12ms/step Epoch 111/400 6/6 - 0s - loss: 0.1198 - accuracy: 0.3986 - val_loss: 0.1179 - val_accuracy: 0.4062 - 76ms/epoch - 13ms/step Epoch 112/400 6/6 - 0s - loss: 0.1191 - accuracy: 0.3968 - val_loss: 0.1177 - val_accuracy: 0.4062 - 80ms/epoch - 13ms/step Epoch 113/400 6/6 - 0s - loss: 0.1189 - accuracy: 0.3977 - val_loss: 0.1174 - val_accuracy: 0.4062 - 74ms/epoch - 12ms/step Epoch 114/400 6/6 - 0s - loss: 0.1191 - accuracy: 0.3986 - val_loss: 0.1171 - val_accuracy: 0.4062 - 75ms/epoch - 12ms/step Epoch 115/400 6/6 - 0s - loss: 0.1188 - accuracy: 0.3995 - val_loss: 0.1165 - val_accuracy: 0.4062 - 62ms/epoch - 10ms/step Epoch 116/400 6/6 - 0s - loss: 0.1167 - accuracy: 0.4227 - val_loss: 0.1153 - val_accuracy: 0.4458 - 64ms/epoch - 11ms/step Epoch 117/400 6/6 - 0s - loss: 0.1162 - accuracy: 0.4281 - val_loss: 0.1128 - val_accuracy: 0.4583 - 77ms/epoch - 13ms/step Epoch 118/400 6/6 - 0s - loss: 0.1128 - accuracy: 0.4513 - val_loss: 0.1091 - val_accuracy: 0.4750 - 82ms/epoch - 14ms/step Epoch 119/400 6/6 - 0s - loss: 0.1084 - accuracy: 0.4745 - val_loss: 0.1021 - val_accuracy: 0.4979 - 77ms/epoch - 13ms/step Epoch 120/400 6/6 - 0s - loss: 0.1081 - accuracy: 0.4710 - val_loss: 0.0965 - val_accuracy: 0.5250 - 82ms/epoch - 14ms/step Epoch 121/400 6/6 - 0s - loss: 0.1057 - accuracy: 0.4772 - val_loss: 0.0885 - val_accuracy: 0.5667 - 79ms/epoch - 13ms/step Epoch 122/400 6/6 - 0s - loss: 0.1007 - accuracy: 0.5058 - val_loss: 0.0852 - val_accuracy: 0.5771 - 83ms/epoch - 14ms/step Epoch 123/400 6/6 - 0s - loss: 0.0973 - accuracy: 0.5228 - val_loss: 0.0847 - val_accuracy: 0.5771 - 80ms/epoch - 13ms/step Epoch 124/400 6/6 - 0s - loss: 0.0931 - accuracy: 0.5389 - val_loss: 0.0828 - val_accuracy: 0.5896 - 77ms/epoch - 13ms/step Epoch 125/400 6/6 - 0s - loss: 0.0940 - accuracy: 0.5326 - val_loss: 0.0810 - val_accuracy: 0.6000 - 89ms/epoch - 15ms/step Epoch 126/400 6/6 - 0s - loss: 0.0906 - accuracy: 0.5505 - val_loss: 0.0816 - val_accuracy: 0.5938 - 78ms/epoch - 13ms/step Epoch 127/400 6/6 - 0s - loss: 0.0900 - accuracy: 0.5523 - val_loss: 0.0819 - val_accuracy: 0.5875 - 73ms/epoch - 12ms/step Epoch 128/400 6/6 - 0s - loss: 0.0894 - accuracy: 0.5684 - val_loss: 0.0819 - val_accuracy: 0.5938 - 63ms/epoch - 10ms/step Epoch 129/400 6/6 - 0s - loss: 0.0900 - accuracy: 0.5559 - val_loss: 0.0824 - val_accuracy: 0.5896 - 59ms/epoch - 10ms/step Epoch 130/400 6/6 - 0s - loss: 0.0884 - accuracy: 0.5630 - val_loss: 0.0808 - val_accuracy: 0.5958 - 58ms/epoch - 10ms/step Epoch 131/400 6/6 - 0s - loss: 0.0900 - accuracy: 0.5541 - val_loss: 0.0791 - val_accuracy: 0.6062 - 44ms/epoch - 7ms/step Epoch 132/400 6/6 - 0s - loss: 0.0880 - accuracy: 0.5675 - val_loss: 0.0796 - val_accuracy: 0.5979 - 60ms/epoch - 10ms/step Epoch 133/400 6/6 - 0s - loss: 0.0867 - accuracy: 0.5702 - val_loss: 0.0802 - val_accuracy: 0.6000 - 45ms/epoch - 7ms/step Epoch 134/400 6/6 - 0s - loss: 0.0863 - accuracy: 0.5746 - val_loss: 0.0805 - val_accuracy: 0.6000 - 59ms/epoch - 10ms/step Epoch 135/400 6/6 - 0s - loss: 0.0866 - accuracy: 0.5710 - val_loss: 0.0804 - val_accuracy: 0.5979 - 45ms/epoch - 7ms/step Epoch 136/400 6/6 - 0s - loss: 0.0860 - accuracy: 0.5719 - val_loss: 0.0799 - val_accuracy: 0.5979 - 44ms/epoch - 7ms/step Epoch 137/400 6/6 - 0s - loss: 0.0852 - accuracy: 0.5755 - val_loss: 0.0806 - val_accuracy: 0.6000 - 44ms/epoch - 7ms/step Epoch 138/400 6/6 - 0s - loss: 0.0873 - accuracy: 0.5675 - val_loss: 0.0805 - val_accuracy: 0.6021 - 61ms/epoch - 10ms/step Epoch 139/400 6/6 - 0s - loss: 0.0839 - accuracy: 0.5871 - val_loss: 0.0791 - val_accuracy: 0.6104 - 48ms/epoch - 8ms/step Epoch 140/400 6/6 - 0s - loss: 0.0854 - accuracy: 0.5755 - val_loss: 0.0779 - val_accuracy: 0.6083 - 43ms/epoch - 7ms/step Epoch 141/400 6/6 - 0s - loss: 0.0835 - accuracy: 0.5871 - val_loss: 0.0773 - val_accuracy: 0.6104 - 46ms/epoch - 8ms/step Epoch 142/400 6/6 - 0s - loss: 0.0832 - accuracy: 0.5889 - val_loss: 0.0777 - val_accuracy: 0.6125 - 59ms/epoch - 10ms/step Epoch 143/400 6/6 - 0s - loss: 0.0837 - accuracy: 0.5845 - val_loss: 0.0787 - val_accuracy: 0.6062 - 44ms/epoch - 7ms/step Epoch 144/400 6/6 - 0s - loss: 0.0841 - accuracy: 0.5853 - val_loss: 0.0807 - val_accuracy: 0.5958 - 59ms/epoch - 10ms/step Epoch 145/400 6/6 - 0s - loss: 0.0827 - accuracy: 0.5880 - val_loss: 0.0824 - val_accuracy: 0.5896 - 43ms/epoch - 7ms/step Epoch 146/400 6/6 - 0s - loss: 0.0828 - accuracy: 0.5925 - val_loss: 0.0825 - val_accuracy: 0.5854 - 44ms/epoch - 7ms/step Epoch 147/400 6/6 - 0s - loss: 0.0823 - accuracy: 0.5889 - val_loss: 0.0822 - val_accuracy: 0.5896 - 46ms/epoch - 8ms/step Epoch 148/400 6/6 - 0s - loss: 0.0815 - accuracy: 0.5916 - val_loss: 0.0812 - val_accuracy: 0.5938 - 51ms/epoch - 8ms/step Epoch 149/400 6/6 - 0s - loss: 0.0822 - accuracy: 0.5934 - val_loss: 0.0806 - val_accuracy: 0.5938 - 49ms/epoch - 8ms/step Epoch 150/400 6/6 - 0s - loss: 0.0841 - accuracy: 0.5836 - val_loss: 0.0815 - val_accuracy: 0.5917 - 57ms/epoch - 9ms/step Epoch 151/400 6/6 - 0s - loss: 0.0837 - accuracy: 0.5871 - val_loss: 0.0815 - val_accuracy: 0.5896 - 44ms/epoch - 7ms/step Epoch 152/400 6/6 - 0s - loss: 0.0817 - accuracy: 0.5979 - val_loss: 0.0800 - val_accuracy: 0.6000 - 59ms/epoch - 10ms/step Epoch 153/400 6/6 - 0s - loss: 0.0820 - accuracy: 0.5934 - val_loss: 0.0792 - val_accuracy: 0.6021 - 45ms/epoch - 7ms/step Epoch 154/400 6/6 - 0s - loss: 0.0830 - accuracy: 0.5871 - val_loss: 0.0799 - val_accuracy: 0.6021 - 44ms/epoch - 7ms/step Epoch 155/400 6/6 - 0s - loss: 0.0804 - accuracy: 0.6014 - val_loss: 0.0801 - val_accuracy: 0.5979 - 43ms/epoch - 7ms/step Epoch 156/400 6/6 - 0s - loss: 0.0830 - accuracy: 0.5871 - val_loss: 0.0801 - val_accuracy: 0.6021 - 43ms/epoch - 7ms/step Epoch 157/400 6/6 - 0s - loss: 0.0806 - accuracy: 0.6032 - val_loss: 0.0807 - val_accuracy: 0.5938 - 45ms/epoch - 7ms/step Epoch 158/400 6/6 - 0s - loss: 0.0806 - accuracy: 0.6005 - val_loss: 0.0811 - val_accuracy: 0.5938 - 60ms/epoch - 10ms/step Epoch 159/400 6/6 - 0s - loss: 0.0803 - accuracy: 0.5987 - val_loss: 0.0808 - val_accuracy: 0.5958 - 58ms/epoch - 10ms/step Epoch 160/400 6/6 - 0s - loss: 0.0805 - accuracy: 0.6014 - val_loss: 0.0807 - val_accuracy: 0.6000 - 44ms/epoch - 7ms/step Epoch 161/400 6/6 - 0s - loss: 0.0816 - accuracy: 0.5979 - val_loss: 0.0816 - val_accuracy: 0.5917 - 53ms/epoch - 9ms/step Epoch 162/400 6/6 - 0s - loss: 0.0791 - accuracy: 0.6130 - val_loss: 0.0821 - val_accuracy: 0.5917 - 46ms/epoch - 8ms/step Epoch 163/400 6/6 - 0s - loss: 0.0820 - accuracy: 0.5898 - val_loss: 0.0823 - val_accuracy: 0.5896 - 49ms/epoch - 8ms/step Epoch 164/400 6/6 - 0s - loss: 0.0795 - accuracy: 0.6041 - val_loss: 0.0818 - val_accuracy: 0.5917 - 47ms/epoch - 8ms/step Epoch 165/400 6/6 - 0s - loss: 0.0829 - accuracy: 0.5880 - val_loss: 0.0816 - val_accuracy: 0.5896 - 44ms/epoch - 7ms/step Epoch 166/400 6/6 - 0s - loss: 0.0793 - accuracy: 0.6041 - val_loss: 0.0809 - val_accuracy: 0.5958 - 44ms/epoch - 7ms/step Epoch 167/400 6/6 - 0s - loss: 0.0794 - accuracy: 0.6086 - val_loss: 0.0809 - val_accuracy: 0.5958 - 58ms/epoch - 10ms/step Epoch 168/400 6/6 - 0s - loss: 0.0812 - accuracy: 0.5961 - val_loss: 0.0815 - val_accuracy: 0.5938 - 46ms/epoch - 8ms/step Epoch 169/400 6/6 - 0s - loss: 0.0817 - accuracy: 0.5943 - val_loss: 0.0813 - val_accuracy: 0.5938 - 58ms/epoch - 10ms/step Epoch 170/400 6/6 - 0s - loss: 0.0808 - accuracy: 0.5952 - val_loss: 0.0818 - val_accuracy: 0.5917 - 43ms/epoch - 7ms/step Epoch 171/400 6/6 - 0s - loss: 0.0790 - accuracy: 0.6130 - val_loss: 0.0813 - val_accuracy: 0.5938 - 44ms/epoch - 7ms/step Epoch 172/400 6/6 - 0s - loss: 0.0792 - accuracy: 0.6059 - val_loss: 0.0808 - val_accuracy: 0.5938 - 59ms/epoch - 10ms/step Epoch 173/400 6/6 - 0s - loss: 0.0793 - accuracy: 0.6059 - val_loss: 0.0804 - val_accuracy: 0.5979 - 45ms/epoch - 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0s - loss: 0.0775 - accuracy: 0.6157 - val_loss: 0.0817 - val_accuracy: 0.5917 - 58ms/epoch - 10ms/step Epoch 183/400 6/6 - 0s - loss: 0.0779 - accuracy: 0.6139 - val_loss: 0.0817 - val_accuracy: 0.5896 - 51ms/epoch - 8ms/step Epoch 184/400 6/6 - 0s - loss: 0.0774 - accuracy: 0.6122 - val_loss: 0.0808 - val_accuracy: 0.5979 - 43ms/epoch - 7ms/step Epoch 185/400 6/6 - 0s - loss: 0.0785 - accuracy: 0.6095 - val_loss: 0.0811 - val_accuracy: 0.5958 - 44ms/epoch - 7ms/step Epoch 186/400 6/6 - 0s - loss: 0.0777 - accuracy: 0.6122 - val_loss: 0.0816 - val_accuracy: 0.5896 - 43ms/epoch - 7ms/step Epoch 187/400 6/6 - 0s - loss: 0.0777 - accuracy: 0.6130 - val_loss: 0.0823 - val_accuracy: 0.5833 - 61ms/epoch - 10ms/step Epoch 188/400 6/6 - 0s - loss: 0.0767 - accuracy: 0.6175 - val_loss: 0.0815 - val_accuracy: 0.5938 - 45ms/epoch - 7ms/step Epoch 189/400 6/6 - 0s - loss: 0.0772 - accuracy: 0.6175 - val_loss: 0.0816 - val_accuracy: 0.5938 - 46ms/epoch - 8ms/step Epoch 190/400 6/6 - 0s - loss: 0.0773 - accuracy: 0.6148 - val_loss: 0.0804 - val_accuracy: 0.6000 - 46ms/epoch - 8ms/step Epoch 191/400 6/6 - 0s - loss: 0.0794 - accuracy: 0.6068 - val_loss: 0.0800 - val_accuracy: 0.6000 - 47ms/epoch - 8ms/step Epoch 192/400 6/6 - 0s - loss: 0.0780 - accuracy: 0.6148 - val_loss: 0.0800 - val_accuracy: 0.5979 - 44ms/epoch - 7ms/step Epoch 193/400 6/6 - 0s - loss: 0.0769 - accuracy: 0.6166 - val_loss: 0.0804 - val_accuracy: 0.5979 - 44ms/epoch - 7ms/step Epoch 194/400 6/6 - 0s - loss: 0.0776 - accuracy: 0.6166 - val_loss: 0.0801 - val_accuracy: 0.5979 - 59ms/epoch - 10ms/step Epoch 195/400 6/6 - 0s - loss: 0.0759 - accuracy: 0.6211 - val_loss: 0.0794 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 196/400 6/6 - 0s - loss: 0.0768 - accuracy: 0.6175 - val_loss: 0.0793 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 197/400 6/6 - 0s - loss: 0.0771 - accuracy: 0.6157 - val_loss: 0.0794 - val_accuracy: 0.6042 - 57ms/epoch - 10ms/step Epoch 198/400 6/6 - 0s - loss: 0.0778 - accuracy: 0.6139 - val_loss: 0.0795 - val_accuracy: 0.6021 - 48ms/epoch - 8ms/step Epoch 199/400 6/6 - 0s - loss: 0.0767 - accuracy: 0.6193 - val_loss: 0.0789 - val_accuracy: 0.6083 - 45ms/epoch - 7ms/step Epoch 200/400 6/6 - 0s - loss: 0.0777 - accuracy: 0.6130 - val_loss: 0.0790 - val_accuracy: 0.6021 - 45ms/epoch - 7ms/step Epoch 201/400 6/6 - 0s - loss: 0.0766 - accuracy: 0.6220 - val_loss: 0.0795 - val_accuracy: 0.6021 - 47ms/epoch - 8ms/step Epoch 202/400 6/6 - 0s - loss: 0.0757 - accuracy: 0.6220 - val_loss: 0.0799 - val_accuracy: 0.6000 - 57ms/epoch - 10ms/step Epoch 203/400 6/6 - 0s - loss: 0.0755 - accuracy: 0.6265 - val_loss: 0.0800 - val_accuracy: 0.5979 - 46ms/epoch - 8ms/step Epoch 204/400 6/6 - 0s - loss: 0.0759 - accuracy: 0.6220 - val_loss: 0.0795 - val_accuracy: 0.6042 - 44ms/epoch - 7ms/step Epoch 205/400 6/6 - 0s - loss: 0.0775 - accuracy: 0.6157 - val_loss: 0.0789 - val_accuracy: 0.6062 - 58ms/epoch - 10ms/step Epoch 206/400 6/6 - 0s - loss: 0.0760 - accuracy: 0.6238 - val_loss: 0.0796 - val_accuracy: 0.6042 - 43ms/epoch - 7ms/step Epoch 207/400 6/6 - 0s - loss: 0.0751 - accuracy: 0.6256 - val_loss: 0.0795 - val_accuracy: 0.6042 - 43ms/epoch - 7ms/step Epoch 208/400 6/6 - 0s - loss: 0.0769 - accuracy: 0.6175 - val_loss: 0.0799 - val_accuracy: 0.6000 - 59ms/epoch - 10ms/step Epoch 209/400 6/6 - 0s - loss: 0.0758 - accuracy: 0.6202 - val_loss: 0.0795 - val_accuracy: 0.6042 - 44ms/epoch - 7ms/step Epoch 210/400 6/6 - 0s - loss: 0.0765 - accuracy: 0.6184 - val_loss: 0.0793 - val_accuracy: 0.6042 - 43ms/epoch - 7ms/step Epoch 211/400 6/6 - 0s - loss: 0.0751 - accuracy: 0.6256 - val_loss: 0.0794 - val_accuracy: 0.6042 - 47ms/epoch - 8ms/step Epoch 212/400 6/6 - 0s - loss: 0.0764 - accuracy: 0.6193 - val_loss: 0.0802 - val_accuracy: 0.5979 - 61ms/epoch - 10ms/step Epoch 213/400 6/6 - 0s - loss: 0.0766 - accuracy: 0.6193 - val_loss: 0.0801 - val_accuracy: 0.6000 - 51ms/epoch - 8ms/step Epoch 214/400 6/6 - 0s - loss: 0.0762 - accuracy: 0.6184 - val_loss: 0.0799 - val_accuracy: 0.6000 - 52ms/epoch - 9ms/step Epoch 215/400 6/6 - 0s - loss: 0.0758 - accuracy: 0.6220 - val_loss: 0.0788 - val_accuracy: 0.6062 - 47ms/epoch - 8ms/step Epoch 216/400 6/6 - 0s - loss: 0.0759 - accuracy: 0.6229 - val_loss: 0.0789 - val_accuracy: 0.6062 - 59ms/epoch - 10ms/step Epoch 217/400 6/6 - 0s - loss: 0.0767 - accuracy: 0.6166 - val_loss: 0.0790 - val_accuracy: 0.6062 - 54ms/epoch - 9ms/step Epoch 218/400 6/6 - 0s - loss: 0.0759 - accuracy: 0.6247 - val_loss: 0.0807 - val_accuracy: 0.5979 - 48ms/epoch - 8ms/step Epoch 219/400 6/6 - 0s - loss: 0.0760 - accuracy: 0.6229 - val_loss: 0.0817 - val_accuracy: 0.5917 - 60ms/epoch - 10ms/step Epoch 220/400 6/6 - 0s - loss: 0.0767 - accuracy: 0.6157 - val_loss: 0.0814 - val_accuracy: 0.5958 - 57ms/epoch - 9ms/step Epoch 221/400 6/6 - 0s - loss: 0.0754 - accuracy: 0.6265 - val_loss: 0.0805 - val_accuracy: 0.5979 - 45ms/epoch - 7ms/step Epoch 222/400 6/6 - 0s - loss: 0.0759 - accuracy: 0.6229 - val_loss: 0.0787 - val_accuracy: 0.6083 - 48ms/epoch - 8ms/step Epoch 223/400 6/6 - 0s - loss: 0.0763 - accuracy: 0.6175 - val_loss: 0.0791 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 224/400 6/6 - 0s - loss: 0.0764 - accuracy: 0.6202 - val_loss: 0.0793 - val_accuracy: 0.6021 - 43ms/epoch - 7ms/step Epoch 225/400 6/6 - 0s - loss: 0.0754 - accuracy: 0.6247 - val_loss: 0.0794 - val_accuracy: 0.6042 - 44ms/epoch - 7ms/step Epoch 226/400 6/6 - 0s - loss: 0.0752 - accuracy: 0.6256 - val_loss: 0.0805 - val_accuracy: 0.5958 - 43ms/epoch - 7ms/step Epoch 227/400 6/6 - 0s - loss: 0.0767 - accuracy: 0.6166 - val_loss: 0.0803 - val_accuracy: 0.5979 - 44ms/epoch - 7ms/step Epoch 228/400 6/6 - 0s - loss: 0.0747 - accuracy: 0.6265 - val_loss: 0.0800 - val_accuracy: 0.5979 - 60ms/epoch - 10ms/step Epoch 229/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6336 - val_loss: 0.0803 - val_accuracy: 0.5979 - 67ms/epoch - 11ms/step Epoch 230/400 6/6 - 0s - loss: 0.0753 - accuracy: 0.6273 - val_loss: 0.0804 - val_accuracy: 0.5958 - 45ms/epoch - 7ms/step Epoch 231/400 6/6 - 0s - loss: 0.0751 - accuracy: 0.6273 - val_loss: 0.0801 - val_accuracy: 0.5979 - 60ms/epoch - 10ms/step Epoch 232/400 6/6 - 0s - loss: 0.0751 - accuracy: 0.6273 - val_loss: 0.0799 - val_accuracy: 0.6000 - 57ms/epoch - 10ms/step Epoch 233/400 6/6 - 0s - loss: 0.0740 - accuracy: 0.6327 - val_loss: 0.0794 - val_accuracy: 0.6042 - 59ms/epoch - 10ms/step Epoch 234/400 6/6 - 0s - loss: 0.0744 - accuracy: 0.6282 - val_loss: 0.0792 - val_accuracy: 0.6042 - 60ms/epoch - 10ms/step Epoch 235/400 6/6 - 0s - loss: 0.0737 - accuracy: 0.6336 - val_loss: 0.0791 - val_accuracy: 0.6042 - 50ms/epoch - 8ms/step Epoch 236/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6309 - val_loss: 0.0794 - val_accuracy: 0.6062 - 63ms/epoch - 11ms/step Epoch 237/400 6/6 - 0s - loss: 0.0746 - accuracy: 0.6300 - val_loss: 0.0789 - val_accuracy: 0.6042 - 45ms/epoch - 8ms/step Epoch 238/400 6/6 - 0s - loss: 0.0751 - accuracy: 0.6291 - val_loss: 0.0780 - val_accuracy: 0.6104 - 47ms/epoch - 8ms/step Epoch 239/400 6/6 - 0s - loss: 0.0740 - accuracy: 0.6318 - val_loss: 0.0779 - val_accuracy: 0.6104 - 46ms/epoch - 8ms/step Epoch 240/400 6/6 - 0s - loss: 0.0754 - accuracy: 0.6256 - val_loss: 0.0793 - val_accuracy: 0.6021 - 47ms/epoch - 8ms/step Epoch 241/400 6/6 - 0s - loss: 0.0749 - accuracy: 0.6291 - val_loss: 0.0795 - val_accuracy: 0.6021 - 43ms/epoch - 7ms/step Epoch 242/400 6/6 - 0s - loss: 0.0747 - accuracy: 0.6282 - val_loss: 0.0802 - val_accuracy: 0.6000 - 59ms/epoch - 10ms/step Epoch 243/400 6/6 - 0s - loss: 0.0748 - accuracy: 0.6273 - val_loss: 0.0805 - val_accuracy: 0.5979 - 61ms/epoch - 10ms/step Epoch 244/400 6/6 - 0s - loss: 0.0738 - accuracy: 0.6327 - val_loss: 0.0803 - val_accuracy: 0.5979 - 52ms/epoch - 9ms/step Epoch 245/400 6/6 - 0s - loss: 0.0752 - accuracy: 0.6256 - val_loss: 0.0808 - val_accuracy: 0.5958 - 57ms/epoch - 9ms/step Epoch 246/400 6/6 - 0s - loss: 0.0748 - accuracy: 0.6300 - val_loss: 0.0816 - val_accuracy: 0.5917 - 59ms/epoch - 10ms/step Epoch 247/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6327 - val_loss: 0.0814 - val_accuracy: 0.5938 - 60ms/epoch - 10ms/step Epoch 248/400 6/6 - 0s - loss: 0.0751 - accuracy: 0.6273 - val_loss: 0.0808 - val_accuracy: 0.5958 - 44ms/epoch - 7ms/step Epoch 249/400 6/6 - 0s - loss: 0.0739 - accuracy: 0.6309 - val_loss: 0.0793 - val_accuracy: 0.6042 - 46ms/epoch - 8ms/step Epoch 250/400 6/6 - 0s - loss: 0.0749 - accuracy: 0.6256 - val_loss: 0.0792 - val_accuracy: 0.6042 - 45ms/epoch - 8ms/step Epoch 251/400 6/6 - 0s - loss: 0.0744 - accuracy: 0.6336 - val_loss: 0.0788 - val_accuracy: 0.6062 - 43ms/epoch - 7ms/step Epoch 252/400 6/6 - 0s - loss: 0.0746 - accuracy: 0.6309 - val_loss: 0.0799 - val_accuracy: 0.6000 - 44ms/epoch - 7ms/step Epoch 253/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6291 - val_loss: 0.0797 - val_accuracy: 0.6021 - 52ms/epoch - 9ms/step Epoch 254/400 6/6 - 0s - loss: 0.0748 - accuracy: 0.6309 - val_loss: 0.0799 - val_accuracy: 0.6021 - 45ms/epoch - 7ms/step Epoch 255/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6354 - val_loss: 0.0779 - val_accuracy: 0.6104 - 57ms/epoch - 9ms/step Epoch 256/400 6/6 - 0s - loss: 0.0745 - accuracy: 0.6309 - val_loss: 0.0778 - val_accuracy: 0.6104 - 43ms/epoch - 7ms/step Epoch 257/400 6/6 - 0s - loss: 0.0744 - accuracy: 0.6318 - val_loss: 0.0787 - val_accuracy: 0.6042 - 63ms/epoch - 11ms/step Epoch 258/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6318 - val_loss: 0.0795 - val_accuracy: 0.6021 - 59ms/epoch - 10ms/step Epoch 259/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6354 - val_loss: 0.0792 - val_accuracy: 0.6042 - 57ms/epoch - 9ms/step Epoch 260/400 6/6 - 0s - loss: 0.0744 - accuracy: 0.6282 - val_loss: 0.0788 - val_accuracy: 0.6062 - 44ms/epoch - 7ms/step Epoch 261/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6327 - val_loss: 0.0795 - val_accuracy: 0.6021 - 45ms/epoch - 7ms/step Epoch 262/400 6/6 - 0s - loss: 0.0744 - accuracy: 0.6282 - val_loss: 0.0802 - val_accuracy: 0.5979 - 44ms/epoch - 7ms/step Epoch 263/400 6/6 - 0s - loss: 0.0740 - accuracy: 0.6291 - val_loss: 0.0809 - val_accuracy: 0.5958 - 42ms/epoch - 7ms/step Epoch 264/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6336 - val_loss: 0.0807 - val_accuracy: 0.5958 - 59ms/epoch - 10ms/step Epoch 265/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6282 - val_loss: 0.0799 - val_accuracy: 0.6000 - 58ms/epoch - 10ms/step Epoch 266/400 6/6 - 0s - loss: 0.0745 - accuracy: 0.6291 - val_loss: 0.0796 - val_accuracy: 0.6021 - 46ms/epoch - 8ms/step Epoch 267/400 6/6 - 0s - loss: 0.0739 - accuracy: 0.6327 - val_loss: 0.0797 - val_accuracy: 0.6021 - 60ms/epoch - 10ms/step Epoch 268/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6327 - val_loss: 0.0806 - val_accuracy: 0.5958 - 46ms/epoch - 8ms/step Epoch 269/400 6/6 - 0s - loss: 0.0740 - accuracy: 0.6336 - val_loss: 0.0791 - val_accuracy: 0.6042 - 59ms/epoch - 10ms/step Epoch 270/400 6/6 - 0s - loss: 0.0738 - accuracy: 0.6345 - val_loss: 0.0785 - val_accuracy: 0.6083 - 60ms/epoch - 10ms/step Epoch 271/400 6/6 - 0s - loss: 0.0733 - accuracy: 0.6354 - val_loss: 0.0786 - val_accuracy: 0.6062 - 68ms/epoch - 11ms/step Epoch 272/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6327 - val_loss: 0.0785 - val_accuracy: 0.6062 - 58ms/epoch - 10ms/step Epoch 273/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6300 - val_loss: 0.0781 - val_accuracy: 0.6104 - 44ms/epoch - 7ms/step Epoch 274/400 6/6 - 0s - loss: 0.0746 - accuracy: 0.6309 - val_loss: 0.0787 - val_accuracy: 0.6062 - 59ms/epoch - 10ms/step Epoch 275/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6381 - val_loss: 0.0800 - val_accuracy: 0.6000 - 46ms/epoch - 8ms/step Epoch 276/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6363 - val_loss: 0.0800 - val_accuracy: 0.6000 - 43ms/epoch - 7ms/step Epoch 277/400 6/6 - 0s - loss: 0.0744 - accuracy: 0.6318 - val_loss: 0.0797 - val_accuracy: 0.6021 - 47ms/epoch - 8ms/step Epoch 278/400 6/6 - 0s - loss: 0.0729 - accuracy: 0.6390 - val_loss: 0.0795 - val_accuracy: 0.6021 - 45ms/epoch - 8ms/step Epoch 279/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6354 - val_loss: 0.0793 - val_accuracy: 0.6021 - 47ms/epoch - 8ms/step Epoch 280/400 6/6 - 0s - loss: 0.0735 - accuracy: 0.6336 - val_loss: 0.0793 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 281/400 6/6 - 0s - loss: 0.0737 - accuracy: 0.6327 - val_loss: 0.0792 - val_accuracy: 0.6062 - 44ms/epoch - 7ms/step Epoch 282/400 6/6 - 0s - loss: 0.0749 - accuracy: 0.6256 - val_loss: 0.0791 - val_accuracy: 0.6042 - 44ms/epoch - 7ms/step Epoch 283/400 6/6 - 0s - loss: 0.0728 - accuracy: 0.6381 - val_loss: 0.0790 - val_accuracy: 0.6062 - 58ms/epoch - 10ms/step Epoch 284/400 6/6 - 0s - loss: 0.0758 - accuracy: 0.6238 - val_loss: 0.0797 - val_accuracy: 0.6000 - 61ms/epoch - 10ms/step Epoch 285/400 6/6 - 0s - loss: 0.0738 - accuracy: 0.6309 - val_loss: 0.0782 - val_accuracy: 0.6083 - 45ms/epoch - 8ms/step Epoch 286/400 6/6 - 0s - loss: 0.0735 - accuracy: 0.6354 - val_loss: 0.0776 - val_accuracy: 0.6125 - 49ms/epoch - 8ms/step Epoch 287/400 6/6 - 0s - loss: 0.0742 - accuracy: 0.6309 - val_loss: 0.0775 - val_accuracy: 0.6125 - 47ms/epoch - 8ms/step Epoch 288/400 6/6 - 0s - loss: 0.0740 - accuracy: 0.6336 - val_loss: 0.0780 - val_accuracy: 0.6104 - 47ms/epoch - 8ms/step Epoch 289/400 6/6 - 0s - loss: 0.0737 - accuracy: 0.6336 - val_loss: 0.0791 - val_accuracy: 0.6042 - 52ms/epoch - 9ms/step Epoch 290/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6363 - val_loss: 0.0792 - val_accuracy: 0.6042 - 46ms/epoch - 8ms/step Epoch 291/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6327 - val_loss: 0.0788 - val_accuracy: 0.6062 - 51ms/epoch - 9ms/step Epoch 292/400 6/6 - 0s - loss: 0.0739 - accuracy: 0.6327 - val_loss: 0.0789 - val_accuracy: 0.6062 - 50ms/epoch - 8ms/step Epoch 293/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6309 - val_loss: 0.0792 - val_accuracy: 0.6042 - 48ms/epoch - 8ms/step Epoch 294/400 6/6 - 0s - loss: 0.0739 - accuracy: 0.6327 - val_loss: 0.0789 - val_accuracy: 0.6062 - 50ms/epoch - 8ms/step Epoch 295/400 6/6 - 0s - loss: 0.0736 - accuracy: 0.6336 - val_loss: 0.0773 - val_accuracy: 0.6125 - 63ms/epoch - 11ms/step Epoch 296/400 6/6 - 0s - loss: 0.0735 - accuracy: 0.6354 - val_loss: 0.0770 - val_accuracy: 0.6146 - 43ms/epoch - 7ms/step Epoch 297/400 6/6 - 0s - loss: 0.0747 - accuracy: 0.6256 - val_loss: 0.0767 - val_accuracy: 0.6167 - 44ms/epoch - 7ms/step Epoch 298/400 6/6 - 0s - loss: 0.0735 - accuracy: 0.6345 - val_loss: 0.0767 - val_accuracy: 0.6167 - 45ms/epoch - 8ms/step Epoch 299/400 6/6 - 0s - loss: 0.0743 - accuracy: 0.6345 - val_loss: 0.0770 - val_accuracy: 0.6167 - 48ms/epoch - 8ms/step Epoch 300/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6363 - val_loss: 0.0768 - val_accuracy: 0.6167 - 60ms/epoch - 10ms/step Epoch 301/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6354 - val_loss: 0.0779 - val_accuracy: 0.6104 - 60ms/epoch - 10ms/step Epoch 302/400 6/6 - 0s - loss: 0.0741 - accuracy: 0.6336 - val_loss: 0.0781 - val_accuracy: 0.6083 - 46ms/epoch - 8ms/step Epoch 303/400 6/6 - 0s - loss: 0.0728 - accuracy: 0.6408 - val_loss: 0.0777 - val_accuracy: 0.6125 - 45ms/epoch - 7ms/step Epoch 304/400 6/6 - 0s - loss: 0.0736 - accuracy: 0.6345 - val_loss: 0.0788 - val_accuracy: 0.6062 - 45ms/epoch - 8ms/step Epoch 305/400 6/6 - 0s - loss: 0.0733 - accuracy: 0.6354 - val_loss: 0.0788 - val_accuracy: 0.6062 - 45ms/epoch - 8ms/step Epoch 306/400 6/6 - 0s - loss: 0.0733 - accuracy: 0.6354 - val_loss: 0.0789 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 307/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6372 - val_loss: 0.0784 - val_accuracy: 0.6083 - 64ms/epoch - 11ms/step Epoch 308/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6363 - val_loss: 0.0785 - val_accuracy: 0.6083 - 49ms/epoch - 8ms/step Epoch 309/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6354 - val_loss: 0.0781 - val_accuracy: 0.6104 - 61ms/epoch - 10ms/step Epoch 310/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6372 - val_loss: 0.0793 - val_accuracy: 0.6021 - 79ms/epoch - 13ms/step Epoch 311/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6416 - val_loss: 0.0791 - val_accuracy: 0.6042 - 68ms/epoch - 11ms/step Epoch 312/400 6/6 - 0s - loss: 0.0729 - accuracy: 0.6390 - val_loss: 0.0791 - val_accuracy: 0.6042 - 78ms/epoch - 13ms/step Epoch 313/400 6/6 - 0s - loss: 0.0722 - accuracy: 0.6390 - val_loss: 0.0783 - val_accuracy: 0.6083 - 66ms/epoch - 11ms/step Epoch 314/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6363 - val_loss: 0.0786 - val_accuracy: 0.6062 - 82ms/epoch - 14ms/step Epoch 315/400 6/6 - 0s - loss: 0.0730 - accuracy: 0.6372 - val_loss: 0.0795 - val_accuracy: 0.6021 - 74ms/epoch - 12ms/step Epoch 316/400 6/6 - 0s - loss: 0.0719 - accuracy: 0.6443 - val_loss: 0.0798 - val_accuracy: 0.6000 - 75ms/epoch - 12ms/step Epoch 317/400 6/6 - 0s - loss: 0.0722 - accuracy: 0.6434 - val_loss: 0.0791 - val_accuracy: 0.6062 - 76ms/epoch - 13ms/step Epoch 318/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6434 - val_loss: 0.0791 - val_accuracy: 0.6042 - 72ms/epoch - 12ms/step Epoch 319/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6372 - val_loss: 0.0777 - val_accuracy: 0.6104 - 75ms/epoch - 12ms/step Epoch 320/400 6/6 - 0s - loss: 0.0730 - accuracy: 0.6381 - val_loss: 0.0793 - val_accuracy: 0.6021 - 74ms/epoch - 12ms/step Epoch 321/400 6/6 - 0s - loss: 0.0725 - accuracy: 0.6408 - val_loss: 0.0801 - val_accuracy: 0.6000 - 79ms/epoch - 13ms/step Epoch 322/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6408 - val_loss: 0.0803 - val_accuracy: 0.5979 - 71ms/epoch - 12ms/step Epoch 323/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6443 - val_loss: 0.0803 - val_accuracy: 0.5979 - 74ms/epoch - 12ms/step Epoch 324/400 6/6 - 0s - loss: 0.0723 - accuracy: 0.6381 - val_loss: 0.0791 - val_accuracy: 0.6021 - 64ms/epoch - 11ms/step Epoch 325/400 6/6 - 0s - loss: 0.0727 - accuracy: 0.6363 - val_loss: 0.0784 - val_accuracy: 0.6104 - 76ms/epoch - 13ms/step Epoch 326/400 6/6 - 0s - loss: 0.0718 - accuracy: 0.6416 - val_loss: 0.0803 - val_accuracy: 0.5979 - 74ms/epoch - 12ms/step Epoch 327/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6372 - val_loss: 0.0804 - val_accuracy: 0.5979 - 64ms/epoch - 11ms/step Epoch 328/400 6/6 - 0s - loss: 0.0728 - accuracy: 0.6363 - val_loss: 0.0794 - val_accuracy: 0.6021 - 74ms/epoch - 12ms/step Epoch 329/400 6/6 - 0s - loss: 0.0718 - accuracy: 0.6416 - val_loss: 0.0787 - val_accuracy: 0.6062 - 78ms/epoch - 13ms/step Epoch 330/400 6/6 - 0s - loss: 0.0732 - accuracy: 0.6354 - val_loss: 0.0782 - val_accuracy: 0.6104 - 78ms/epoch - 13ms/step Epoch 331/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6408 - val_loss: 0.0796 - val_accuracy: 0.6021 - 77ms/epoch - 13ms/step Epoch 332/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6416 - val_loss: 0.0807 - val_accuracy: 0.5958 - 75ms/epoch - 12ms/step Epoch 333/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6425 - val_loss: 0.0805 - val_accuracy: 0.5979 - 75ms/epoch - 13ms/step Epoch 334/400 6/6 - 0s - loss: 0.0729 - accuracy: 0.6390 - val_loss: 0.0788 - val_accuracy: 0.6062 - 75ms/epoch - 12ms/step Epoch 335/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6425 - val_loss: 0.0788 - val_accuracy: 0.6062 - 74ms/epoch - 12ms/step Epoch 336/400 6/6 - 0s - loss: 0.0723 - accuracy: 0.6416 - val_loss: 0.0788 - val_accuracy: 0.6062 - 73ms/epoch - 12ms/step Epoch 337/400 6/6 - 0s - loss: 0.0714 - accuracy: 0.6452 - val_loss: 0.0789 - val_accuracy: 0.6062 - 68ms/epoch - 11ms/step Epoch 338/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6408 - val_loss: 0.0799 - val_accuracy: 0.6000 - 80ms/epoch - 13ms/step Epoch 339/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6443 - val_loss: 0.0806 - val_accuracy: 0.5958 - 65ms/epoch - 11ms/step Epoch 340/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6452 - val_loss: 0.0801 - val_accuracy: 0.6000 - 70ms/epoch - 12ms/step Epoch 341/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6425 - val_loss: 0.0795 - val_accuracy: 0.6042 - 73ms/epoch - 12ms/step Epoch 342/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6408 - val_loss: 0.0793 - val_accuracy: 0.6021 - 62ms/epoch - 10ms/step Epoch 343/400 6/6 - 0s - loss: 0.0714 - accuracy: 0.6425 - val_loss: 0.0794 - val_accuracy: 0.6021 - 78ms/epoch - 13ms/step Epoch 344/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6425 - val_loss: 0.0789 - val_accuracy: 0.6062 - 80ms/epoch - 13ms/step Epoch 345/400 6/6 - 0s - loss: 0.0722 - accuracy: 0.6399 - val_loss: 0.0790 - val_accuracy: 0.6042 - 75ms/epoch - 13ms/step Epoch 346/400 6/6 - 0s - loss: 0.0716 - accuracy: 0.6425 - val_loss: 0.0785 - val_accuracy: 0.6062 - 68ms/epoch - 11ms/step Epoch 347/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6425 - val_loss: 0.0802 - val_accuracy: 0.5979 - 74ms/epoch - 12ms/step Epoch 348/400 6/6 - 0s - loss: 0.0710 - accuracy: 0.6470 - val_loss: 0.0802 - val_accuracy: 0.6000 - 78ms/epoch - 13ms/step Epoch 349/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6416 - val_loss: 0.0796 - val_accuracy: 0.6000 - 68ms/epoch - 11ms/step Epoch 350/400 6/6 - 0s - loss: 0.0710 - accuracy: 0.6479 - val_loss: 0.0796 - val_accuracy: 0.6021 - 71ms/epoch - 12ms/step Epoch 351/400 6/6 - 0s - loss: 0.0712 - accuracy: 0.6461 - val_loss: 0.0793 - val_accuracy: 0.6042 - 65ms/epoch - 11ms/step Epoch 352/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6399 - val_loss: 0.0790 - val_accuracy: 0.6042 - 74ms/epoch - 12ms/step Epoch 353/400 6/6 - 0s - loss: 0.0726 - accuracy: 0.6399 - val_loss: 0.0791 - val_accuracy: 0.6042 - 75ms/epoch - 12ms/step Epoch 354/400 6/6 - 0s - loss: 0.0707 - accuracy: 0.6479 - val_loss: 0.0784 - val_accuracy: 0.6083 - 59ms/epoch - 10ms/step Epoch 355/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6425 - val_loss: 0.0786 - val_accuracy: 0.6083 - 46ms/epoch - 8ms/step Epoch 356/400 6/6 - 0s - loss: 0.0711 - accuracy: 0.6452 - val_loss: 0.0782 - val_accuracy: 0.6104 - 49ms/epoch - 8ms/step Epoch 357/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6434 - val_loss: 0.0783 - val_accuracy: 0.6083 - 45ms/epoch - 7ms/step Epoch 358/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6425 - val_loss: 0.0799 - val_accuracy: 0.6000 - 45ms/epoch - 7ms/step Epoch 359/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6434 - val_loss: 0.0800 - val_accuracy: 0.6000 - 45ms/epoch - 8ms/step Epoch 360/400 6/6 - 0s - loss: 0.0719 - accuracy: 0.6416 - val_loss: 0.0799 - val_accuracy: 0.6021 - 61ms/epoch - 10ms/step Epoch 361/400 6/6 - 0s - loss: 0.0714 - accuracy: 0.6461 - val_loss: 0.0792 - val_accuracy: 0.6042 - 60ms/epoch - 10ms/step Epoch 362/400 6/6 - 0s - loss: 0.0730 - accuracy: 0.6354 - val_loss: 0.0779 - val_accuracy: 0.6104 - 48ms/epoch - 8ms/step Epoch 363/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6416 - val_loss: 0.0769 - val_accuracy: 0.6167 - 54ms/epoch - 9ms/step Epoch 364/400 6/6 - 0s - loss: 0.0718 - accuracy: 0.6416 - val_loss: 0.0775 - val_accuracy: 0.6104 - 60ms/epoch - 10ms/step Epoch 365/400 6/6 - 0s - loss: 0.0714 - accuracy: 0.6452 - val_loss: 0.0785 - val_accuracy: 0.6083 - 47ms/epoch - 8ms/step Epoch 366/400 6/6 - 0s - loss: 0.0713 - accuracy: 0.6470 - val_loss: 0.0795 - val_accuracy: 0.6021 - 60ms/epoch - 10ms/step Epoch 367/400 6/6 - 0s - loss: 0.0712 - accuracy: 0.6443 - val_loss: 0.0798 - val_accuracy: 0.6021 - 51ms/epoch - 9ms/step Epoch 368/400 6/6 - 0s - loss: 0.0716 - accuracy: 0.6443 - val_loss: 0.0794 - val_accuracy: 0.6021 - 59ms/epoch - 10ms/step Epoch 369/400 6/6 - 0s - loss: 0.0713 - accuracy: 0.6470 - val_loss: 0.0791 - val_accuracy: 0.6021 - 49ms/epoch - 8ms/step Epoch 370/400 6/6 - 0s - loss: 0.0707 - accuracy: 0.6470 - val_loss: 0.0779 - val_accuracy: 0.6104 - 59ms/epoch - 10ms/step Epoch 371/400 6/6 - 0s - loss: 0.0711 - accuracy: 0.6497 - val_loss: 0.0780 - val_accuracy: 0.6104 - 61ms/epoch - 10ms/step Epoch 372/400 6/6 - 0s - loss: 0.0711 - accuracy: 0.6452 - val_loss: 0.0793 - val_accuracy: 0.6021 - 49ms/epoch - 8ms/step Epoch 373/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6452 - val_loss: 0.0792 - val_accuracy: 0.6042 - 50ms/epoch - 8ms/step Epoch 374/400 6/6 - 0s - loss: 0.0719 - accuracy: 0.6408 - val_loss: 0.0794 - val_accuracy: 0.6021 - 61ms/epoch - 10ms/step Epoch 375/400 6/6 - 0s - loss: 0.0711 - accuracy: 0.6461 - val_loss: 0.0773 - val_accuracy: 0.6125 - 47ms/epoch - 8ms/step Epoch 376/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6416 - val_loss: 0.0764 - val_accuracy: 0.6167 - 61ms/epoch - 10ms/step Epoch 377/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6416 - val_loss: 0.0761 - val_accuracy: 0.6187 - 46ms/epoch - 8ms/step Epoch 378/400 6/6 - 0s - loss: 0.0712 - accuracy: 0.6488 - val_loss: 0.0773 - val_accuracy: 0.6146 - 44ms/epoch - 7ms/step Epoch 379/400 6/6 - 0s - loss: 0.0708 - accuracy: 0.6479 - val_loss: 0.0791 - val_accuracy: 0.6042 - 48ms/epoch - 8ms/step Epoch 380/400 6/6 - 0s - loss: 0.0707 - accuracy: 0.6497 - val_loss: 0.0792 - val_accuracy: 0.6042 - 54ms/epoch - 9ms/step Epoch 381/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6425 - val_loss: 0.0789 - val_accuracy: 0.6062 - 46ms/epoch - 8ms/step Epoch 382/400 6/6 - 0s - loss: 0.0722 - accuracy: 0.6416 - val_loss: 0.0793 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 383/400 6/6 - 0s - loss: 0.0719 - accuracy: 0.6425 - val_loss: 0.0803 - val_accuracy: 0.5979 - 58ms/epoch - 10ms/step Epoch 384/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6452 - val_loss: 0.0795 - val_accuracy: 0.6021 - 44ms/epoch - 7ms/step Epoch 385/400 6/6 - 0s - loss: 0.0710 - accuracy: 0.6461 - val_loss: 0.0794 - val_accuracy: 0.6042 - 59ms/epoch - 10ms/step Epoch 386/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6372 - val_loss: 0.0796 - val_accuracy: 0.6021 - 53ms/epoch - 9ms/step Epoch 387/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6425 - val_loss: 0.0797 - val_accuracy: 0.6021 - 45ms/epoch - 7ms/step Epoch 388/400 6/6 - 0s - loss: 0.0720 - accuracy: 0.6416 - val_loss: 0.0793 - val_accuracy: 0.6042 - 45ms/epoch - 7ms/step Epoch 389/400 6/6 - 0s - loss: 0.0722 - accuracy: 0.6408 - val_loss: 0.0793 - val_accuracy: 0.6042 - 45ms/epoch - 8ms/step Epoch 390/400 6/6 - 0s - loss: 0.0706 - accuracy: 0.6488 - val_loss: 0.0786 - val_accuracy: 0.6062 - 46ms/epoch - 8ms/step Epoch 391/400 6/6 - 0s - loss: 0.0713 - accuracy: 0.6479 - val_loss: 0.0791 - val_accuracy: 0.6021 - 62ms/epoch - 10ms/step Epoch 392/400 6/6 - 0s - loss: 0.0717 - accuracy: 0.6425 - val_loss: 0.0789 - val_accuracy: 0.6042 - 58ms/epoch - 10ms/step Epoch 393/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6434 - val_loss: 0.0789 - val_accuracy: 0.6062 - 50ms/epoch - 8ms/step Epoch 394/400 6/6 - 0s - loss: 0.0711 - accuracy: 0.6497 - val_loss: 0.0798 - val_accuracy: 0.6021 - 64ms/epoch - 11ms/step Epoch 395/400 6/6 - 0s - loss: 0.0721 - accuracy: 0.6399 - val_loss: 0.0787 - val_accuracy: 0.6062 - 47ms/epoch - 8ms/step Epoch 396/400 6/6 - 0s - loss: 0.0715 - accuracy: 0.6461 - val_loss: 0.0771 - val_accuracy: 0.6146 - 46ms/epoch - 8ms/step Epoch 397/400 6/6 - 0s - loss: 0.0727 - accuracy: 0.6381 - val_loss: 0.0765 - val_accuracy: 0.6187 - 60ms/epoch - 10ms/step Epoch 398/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6345 - val_loss: 0.0777 - val_accuracy: 0.6104 - 50ms/epoch - 8ms/step Epoch 399/400 6/6 - 0s - loss: 0.0731 - accuracy: 0.6354 - val_loss: 0.0773 - val_accuracy: 0.6146 - 47ms/epoch - 8ms/step Epoch 400/400 6/6 - 0s - loss: 0.0724 - accuracy: 0.6408 - val_loss: 0.0771 - val_accuracy: 0.6167 - 45ms/epoch - 8ms/step
# predicting the model on test data
y_pred=model.predict(x_test)
15/15 [==============================] - 0s 2ms/step
y_pred[0]
array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
0.0000000e+00, 1.7189235e-23, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, 0.0000000e+00], dtype=float32)
# Since the outputs are in probabilities we try to get the label
y_pred_final=[]
for i in y_pred:
y_pred_final.append(np.argmax(i))
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred_final))
precision recall f1-score support
3 0.00 0.00 0.00 2
4 0.00 0.00 0.00 21
5 0.71 0.73 0.72 207
6 0.55 0.74 0.63 195
7 0.00 0.00 0.00 52
8 0.00 0.00 0.00 3
accuracy 0.62 480
macro avg 0.21 0.25 0.22 480
weighted avg 0.53 0.62 0.56 480
from sklearn.metrics import confusion_matrix #Confusion matrix
import seaborn as sns
cm=confusion_matrix(y_test,y_pred_final)
plt.figure(figsize=(10,7))
sns.heatmap(cm,annot=True,fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')
plt.show()
import matplotlib.pyplot as plt #plotting the accuracy and losses after each iteration
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string])
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
Insights on difference between the two models:
import h5py
h5f = h5py.File('/content/MyDrive/MyDrive/Dataset/Autonomous_Vehicles_SVHN_single_grey1.h5', 'r') #reading the h5py file
x_train = h5f['X_train'][:] #loading the train and test data
y_train = h5f['y_train'][:]
x_test = h5f['X_test'][:]
y_test = h5f['y_test'][:]
h5f.close() #closing the file
print('X Train set contains {} data'.format(x_train.shape))
print('X Test set contains {} data'.format(x_test.shape)) #shapeof the train and test data
print('Y Train set contains {} data'.format(y_train.shape))
print('Y Test set contains {} data'.format(y_test.shape))
X Train set contains (42000, 32, 32) data X Test set contains (18000, 32, 32) data Y Train set contains (42000,) data Y Test set contains (18000,) data
plt.figure(figsize=(10, 1)) #first 10 images from the dataset
for i in range(10):
plt.subplot(1, 10, i+1)
plt.imshow(x_train[i], cmap="gray")
plt.axis('off')
plt.show()
print('label for each of the above image: %s' % (y_train[0:10]))
label for each of the above image: [2 6 7 4 4 0 3 0 7 3]
image_size = 32*32
x_train = x_train.reshape(x_train.shape[0], image_size) #Reshaping the images
x_test = x_test.reshape(x_test.shape[0], image_size)
# normalize inputs from 0-255 to 0-1
x_train = x_train / 255.0
x_test = x_test / 255.0
print('Training set', x_train.shape, y_train.shape)
print('Test set', x_test.shape, y_test.shape)
Training set (42000, 1024) (42000,) Test set (18000, 1024) (18000,)
np.unique(y_train) #target variable labels
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)
num_classes = 10 #convert the target variable to one hot vectors
y_train_cat = to_categorical(y_train, num_classes)
y_test_cat=to_categorical(y_test,num_classes)
print("First 5 training lables as one-hot encoded vectors:\n", y_train_cat[:5])
First 5 training lables as one-hot encoded vectors: [[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]
print("Label: ", y_train[1]) #example
print("label:", y_train_cat[1])
plt.imshow(x_train[1].reshape(32,32), cmap='gray')
Label: 6 label: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
<matplotlib.image.AxesImage at 0x7fc90254e140>
model = Sequential()
model.add(Dense(512, activation='relu',kernel_initializer='he_uniform',input_shape=(image_size,))) ###Multiple Dense layers with Relu activation
model.add(Dense(128, activation='relu',kernel_initializer='he_uniform'))
#model.add(Dense(64, activation='relu',kernel_initializer='he_uniform'))
#model.add(Dense(32, activation='relu',kernel_initializer='he_uniform'))
model.add(Dense(num_classes, activation='softmax')) ### For multiclass classification Softmax is used
# Compile model
adam = optimizers.Adam(lr=1e-3)
model.compile(loss=losses.categorical_crossentropy, optimizer=adam, metrics=['accuracy']) ### Loss function = Categorical cross entropy
model.summary() #Summary of neural network model
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_10 (Dense) (None, 512) 524800
dense_11 (Dense) (None, 128) 65664
dense_12 (Dense) (None, 10) 1290
=================================================================
Total params: 591,754
Trainable params: 591,754
Non-trainable params: 0
_________________________________________________________________
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau #callback
checkpoint = ModelCheckpoint("model_weights.h5",monitor='val_accuracy',
save_weights_only=True, mode='max',verbose=1)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',factor=0.1,patience=2,min_lr=0.00001,model='auto')
callbacks = [checkpoint,reduce_lr]
# Fit the model
history=model.fit(x_train, y_train_cat, validation_split=0.2, epochs=30, batch_size=128, verbose=2,callbacks=callbacks)
Epoch 1/30 Epoch 1: saving model to model_weights.h5 263/263 - 1s - loss: 2.3056 - accuracy: 0.1285 - val_loss: 2.2171 - val_accuracy: 0.1961 - lr: 0.0010 - 1s/epoch - 5ms/step Epoch 2/30 Epoch 2: saving model to model_weights.h5 263/263 - 1s - loss: 1.8350 - accuracy: 0.3657 - val_loss: 1.5143 - val_accuracy: 0.5121 - lr: 0.0010 - 799ms/epoch - 3ms/step Epoch 3/30 Epoch 3: saving model to model_weights.h5 263/263 - 1s - loss: 1.3865 - accuracy: 0.5565 - val_loss: 1.3361 - val_accuracy: 0.5874 - lr: 0.0010 - 800ms/epoch - 3ms/step Epoch 4/30 Epoch 4: saving model to model_weights.h5 263/263 - 1s - loss: 1.2226 - accuracy: 0.6194 - val_loss: 1.1900 - val_accuracy: 0.6194 - lr: 0.0010 - 798ms/epoch - 3ms/step Epoch 5/30 Epoch 5: saving model to model_weights.h5 263/263 - 1s - loss: 1.1209 - accuracy: 0.6510 - val_loss: 1.0789 - val_accuracy: 0.6651 - lr: 0.0010 - 792ms/epoch - 3ms/step Epoch 6/30 Epoch 6: saving model to model_weights.h5 263/263 - 1s - loss: 1.0561 - accuracy: 0.6720 - val_loss: 1.0878 - val_accuracy: 0.6669 - lr: 0.0010 - 793ms/epoch - 3ms/step Epoch 7/30 Epoch 7: saving model to model_weights.h5 263/263 - 1s - loss: 0.9959 - accuracy: 0.6934 - val_loss: 0.9875 - val_accuracy: 0.7024 - lr: 0.0010 - 938ms/epoch - 4ms/step Epoch 8/30 Epoch 8: saving model to model_weights.h5 263/263 - 1s - loss: 0.9571 - accuracy: 0.7070 - val_loss: 1.0552 - val_accuracy: 0.6844 - lr: 0.0010 - 830ms/epoch - 3ms/step Epoch 9/30 Epoch 9: saving model to model_weights.h5 263/263 - 1s - loss: 0.9009 - accuracy: 0.7251 - val_loss: 0.9020 - val_accuracy: 0.7275 - lr: 0.0010 - 948ms/epoch - 4ms/step Epoch 10/30 Epoch 10: saving model to model_weights.h5 263/263 - 1s - loss: 0.8773 - accuracy: 0.7325 - val_loss: 0.8980 - val_accuracy: 0.7267 - lr: 0.0010 - 1s/epoch - 4ms/step Epoch 11/30 Epoch 11: saving model to model_weights.h5 263/263 - 1s - loss: 0.8417 - accuracy: 0.7437 - val_loss: 0.8797 - val_accuracy: 0.7369 - lr: 0.0010 - 1s/epoch - 4ms/step Epoch 12/30 Epoch 12: saving model to model_weights.h5 263/263 - 1s - loss: 0.8199 - accuracy: 0.7510 - val_loss: 0.8385 - val_accuracy: 0.7429 - lr: 0.0010 - 1s/epoch - 5ms/step Epoch 13/30 Epoch 13: saving model to model_weights.h5 263/263 - 1s - loss: 0.7911 - accuracy: 0.7588 - val_loss: 0.8472 - val_accuracy: 0.7410 - lr: 0.0010 - 812ms/epoch - 3ms/step Epoch 14/30 Epoch 14: saving model to model_weights.h5 263/263 - 1s - loss: 0.7868 - accuracy: 0.7593 - val_loss: 0.8698 - val_accuracy: 0.7337 - lr: 0.0010 - 804ms/epoch - 3ms/step Epoch 15/30 Epoch 15: saving model to model_weights.h5 263/263 - 1s - loss: 0.6863 - accuracy: 0.7952 - val_loss: 0.7484 - val_accuracy: 0.7801 - lr: 1.0000e-04 - 792ms/epoch - 3ms/step Epoch 16/30 Epoch 16: saving model to model_weights.h5 263/263 - 1s - loss: 0.6774 - accuracy: 0.7970 - val_loss: 0.7489 - val_accuracy: 0.7810 - lr: 1.0000e-04 - 793ms/epoch - 3ms/step Epoch 17/30 Epoch 17: saving model to model_weights.h5 263/263 - 1s - loss: 0.6734 - accuracy: 0.7980 - val_loss: 0.7439 - val_accuracy: 0.7787 - lr: 1.0000e-04 - 802ms/epoch - 3ms/step Epoch 18/30 Epoch 18: saving model to model_weights.h5 263/263 - 1s - loss: 0.6710 - accuracy: 0.7984 - val_loss: 0.7395 - val_accuracy: 0.7852 - lr: 1.0000e-04 - 799ms/epoch - 3ms/step Epoch 19/30 Epoch 19: saving model to model_weights.h5 263/263 - 1s - loss: 0.6672 - accuracy: 0.8007 - val_loss: 0.7395 - val_accuracy: 0.7830 - lr: 1.0000e-04 - 801ms/epoch - 3ms/step Epoch 20/30 Epoch 20: saving model to model_weights.h5 263/263 - 1s - loss: 0.6638 - accuracy: 0.8016 - val_loss: 0.7356 - val_accuracy: 0.7854 - lr: 1.0000e-04 - 838ms/epoch - 3ms/step Epoch 21/30 Epoch 21: saving model to model_weights.h5 263/263 - 1s - loss: 0.6613 - accuracy: 0.8040 - val_loss: 0.7372 - val_accuracy: 0.7842 - lr: 1.0000e-04 - 787ms/epoch - 3ms/step Epoch 22/30 Epoch 22: saving model to model_weights.h5 263/263 - 1s - loss: 0.6587 - accuracy: 0.8038 - val_loss: 0.7350 - val_accuracy: 0.7854 - lr: 1.0000e-04 - 800ms/epoch - 3ms/step Epoch 23/30 Epoch 23: saving model to model_weights.h5 263/263 - 1s - loss: 0.6549 - accuracy: 0.8057 - val_loss: 0.7352 - val_accuracy: 0.7845 - lr: 1.0000e-04 - 786ms/epoch - 3ms/step Epoch 24/30 Epoch 24: saving model to model_weights.h5 263/263 - 1s - loss: 0.6530 - accuracy: 0.8059 - val_loss: 0.7328 - val_accuracy: 0.7832 - lr: 1.0000e-04 - 798ms/epoch - 3ms/step Epoch 25/30 Epoch 25: saving model to model_weights.h5 263/263 - 1s - loss: 0.6508 - accuracy: 0.8060 - val_loss: 0.7287 - val_accuracy: 0.7863 - lr: 1.0000e-04 - 1s/epoch - 4ms/step Epoch 26/30 Epoch 26: saving model to model_weights.h5 263/263 - 1s - loss: 0.6465 - accuracy: 0.8077 - val_loss: 0.7296 - val_accuracy: 0.7858 - lr: 1.0000e-04 - 1s/epoch - 4ms/step Epoch 27/30 Epoch 27: saving model to model_weights.h5 263/263 - 1s - loss: 0.6445 - accuracy: 0.8083 - val_loss: 0.7257 - val_accuracy: 0.7857 - lr: 1.0000e-04 - 1s/epoch - 5ms/step Epoch 28/30 Epoch 28: saving model to model_weights.h5 263/263 - 1s - loss: 0.6427 - accuracy: 0.8084 - val_loss: 0.7288 - val_accuracy: 0.7840 - lr: 1.0000e-04 - 828ms/epoch - 3ms/step Epoch 29/30 Epoch 29: saving model to model_weights.h5 263/263 - 1s - loss: 0.6393 - accuracy: 0.8106 - val_loss: 0.7249 - val_accuracy: 0.7885 - lr: 1.0000e-04 - 784ms/epoch - 3ms/step Epoch 30/30 Epoch 30: saving model to model_weights.h5 263/263 - 1s - loss: 0.6385 - accuracy: 0.8096 - val_loss: 0.7202 - val_accuracy: 0.7907 - lr: 1.0000e-04 - 782ms/epoch - 3ms/step
# predicting the model on test data
y_pred=model.predict(x_test)
563/563 [==============================] - 1s 1ms/step
# Since the outputs are probability values we try to get the labels
y_pred_final=[]
for i in y_pred:
y_pred_final.append(np.argmax(i))
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred_final))
precision recall f1-score support
0 0.84 0.81 0.82 1814
1 0.79 0.82 0.81 1828
2 0.83 0.79 0.81 1803
3 0.74 0.73 0.73 1719
4 0.84 0.82 0.83 1812
5 0.69 0.79 0.74 1768
6 0.79 0.77 0.78 1832
7 0.81 0.83 0.82 1808
8 0.78 0.71 0.74 1812
9 0.75 0.77 0.76 1804
accuracy 0.78 18000
macro avg 0.78 0.78 0.78 18000
weighted avg 0.79 0.78 0.78 18000
from sklearn.metrics import confusion_matrix #confusion matrix
import seaborn as sns
cm=confusion_matrix(y_test,y_pred_final)
plt.figure(figsize=(10,7))
sns.heatmap(cm,annot=True,fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')
plt.show()
import matplotlib.pyplot as plt
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string]) #plotting the accuracy and losses after each iteration
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
We can see that the model is not overfit. The training and validation accuracy and losses are close to each other.